Press release

Preferred Networks unveils a personal robot system at CEATEC Japan 2018, exhibiting fully-autonomous tidying-up robots

Oct. 15, 2018, Tokyo Japan – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) will unveil a fully-autonomous tidying-up robot system, which is currently under development, at the CEATEC Japan 2018 exhibition held in Makuhari Messe near Tokyo. A technical demonstration of the system will be given at the event.

PFN is developing technology to create a society where robots can actively support our daily living activities. Unlike in controlled and regulated environments like factories, robots in the home need to respond flexibly to dynamic and complex situations and communicate naturally with humans.

At its exhibition booth (A060), PFN will demonstrate the new robot system using HSRs (Human Support Robots) developed by Toyota Motor Corporation and showcase their ability to keep a cluttered room neat and tidy. This has been difficult to achieve using conventional technologies based on object recognition and robot control. The deep learning-based robots can recognize various scattered household items like clothes, toys, and stationery, grasp and place them in their designated locations. In the demonstration, PFN will also show that these cleaning robots can be controlled intuitively through verbal and gestural instructions.

For technical details, please visit the website.
https://projects.preferred.jp/tidying-up-robot/en

 


The fully-autonomous tidying-up robot system has been awarded the Semi-Grand Prix in the Industry/Market category at CEATEC Award 2018, which recognizes innovative technologies, products, and services from among a large number of exhibits at CEATEC JAPAN 2018.

 

  • PFN exhibition booth

・Dates/time:10:00–17:00 Oct. 16–19, 2018

・Location:Booth A060, Total Solutions Area, International Exhibition Hall 2

・Exhibit:Technical demonstration of personal robots “fully-autonomous, tidying-up robot system” (first public exhibition)

 

In addition, PFN President and CEO Toru Nishikawa will make a keynote speech entitled “Robots for Everyone” on the opening day of the CEATEC exhibition. As well as introducing the outlook on future technologies, he will explain how PFN is applying cutting-edge technologies of machine learning, deep learning, and robotics to solve real-world problems.

  • CEATEC Keynote Future
  • ・Date/Time: 12:30~13:15 Tuesday on Oct. 16, 2018・Location:Convention Hall, International Conference Hall, Makuhari Messe・Speaker:Preferred Networks President and CEO Toru Nishikawa・Title/outline:”Robots for Everyone”The possibilities of robots are rapidly expanding thanks to the advancement of machine learning technology. Fusing the machine learning technology with robotics is essential for making robots that can flexibly respond to unexpected situations and execute various tasks like a human. Soon, we will begin to see an increasing number of robots helping to perform tasks in many places, working alongside humans. As well as introducing the current technology and PFN’s new initiative, President Nishikawa will provide insight into how we can leverage technology in the new era of these kinds of robots and the outlook on future technologies.

Preferred Networks releases deep learning-based, high-precision, visual inspection software

 

The software will make it possible to construct an inspection system quickly and inexpensively with minimal training datasets

 

Oct. 11, 2018, Tokyo Japan – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has developed Preferred Networks Visual Inspection,  high-precision, visual inspection software based on deep learning technology. PFN will start licensing the software to partner companies in December 2018. We will announce the new software at a new product seminar (N3-5) on Thursday Oct.18 during the CEATEC Japan 2018 exhibition held in Makuhari Messe near Tokyo.

The use of machine learning and deep learning technologies is spreading rapidly in many areas including the manufacturing floor. However, existing visual inspection systems based on deep learning require as many as several thousand images for training, as well as engineers to annotate the considerable number of images to facilitate the training process. Poorly explained inspection results are also among other issues that have been tackled.

In order to solve these problems, PFN has utilized its technical know-how acquired through the development of the deep learning framework Chainer(TM) and applications of deep learning to our main business domains – transportation systems, manufacturing, and bio-healthcare – to develop the Preferred Networks Visual Inspection.

  • The main features of Preferred Networks Visual Inspection:
  1. An inspection line can be set up with a small amount of training data (as few as 100 images of normal products and 20 images of defective products)
  2. Plastic, metal, cloth, food, and other materials with various shapes can be handled
  3. Results are well-explained through visualized anomalies such as scratches, foreign objects, and stains
  4. Training is made easy even for non-engineers with intuitive user interfaces

 

Preferred Networks Visual Inspection consists of a training support tool and CPU-based defect detection software. Depending on requirements, our licensed partners will install a combination of system components which include training workstations, inspection PCs, photographing equipment, UIs for visualization and operation. GPU-based, fast detection software is also available as an option.

The new product will enable users to build an easy-to-use and highly reliable auto-inspection system at a low cost in a short period of time. This product can be introduced with ease to the manufacturing lines which have been difficult to automate by existing products due to their high costs and inflexibility. In addition, defects are visualized so that its results can be easily explained. This is useful for passing down inspection skills and sharing knowledge with others in the company.

Comparison of Preferred Networks Visual Inspection and the existing solutions

  • New product announcement

PFN will announce Preferred Networks Visual Inspection at a New Technologies and Products Seminar (N3-5) entitled “Visual inspection system and picking robot solution based on deep learning” at CEATEC Japan in Makuhari Messe.

 

PFN will continue to promote practical applications of machine learning and deep learning technologies in the real world.

Preferred Networks wins second place in the Google AI Open Images – Object Detection Track, competed with 454 teams

Sept. 7, 2018, Tokyo Japan – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) participated in the Google AI Open Images – Object Detection Track, an object detection challenge hosted by Kaggle*1, and won second place in the competition among 454 teams from around the world.

 

Object detection, which is one of the major research subjects in computer vision, is a basic technology that is critical for autonomous driving and robotics. Challenges in using large-scale datasets, such as ImageNet and MS COCO, to achieve better accuracy in object detection have been the unifying force of the research community, contributing to the rapid improvement of detection techniques and algorithms.

 

The Google AI Open Images – Object Detection Track held between July 3, 2018 and August 30, 2018 was a competition of an unprecedented scale that used Open Images V4*2, a large and complex dataset released by Google this year. As a result, the event attracted the attention of many researchers. A total of 454 teams from around the world participated in the competition.

PFN entered the competition as team “PFDet”, comprising interested members, mainly developers of ChainerMN and ChainerCV, PFN’s distributed deep learning library and computer vision library based on deep learning, respectively, as well as specialists in the fields of autonomous driving and robotics. During the competition, PFN’s large-scale cluster MN-1b that has 512 NVIDIA (R) Tesla(R) V100 32GB GPUs was in full operation for the first time since its launch in July this year. In addition, the team utilized a parallel deep learning technique to speed up training with a large-scale dataset and made full use of research results PFN had accumulated over the years in the fields of autonomous driving and robotics.  These efforts resulted in the team finishing in a close second place by a narrow margin of 0.023% behind the team who won first place.

 

We have published a paper, entitled “PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track,” regarding our solution method in this competition, at https://arxiv.org/abs/1809.00778

We also plan to present the content of the paper at a workshop at the European Conference on Computer Vision (ECCV)2018.

 

A part of the techniques developed for this competition will be released as additional functionality to ChainerMN and ChainerCV.

 

PFN will continue to work on research and development of image analysis and object detection technologies, and promote their practical applications in our three primary business domains, namely, transportation, manufacturing, and bio/healthcare.

 

*1:A platform for machine learning competitions

*2:A very large training dataset comprised of 1.7 million images (including 12 million objects of 500 classes)

Preferred Networks raises a total of about 900 million yen in capital from Chugai Pharmaceutical and Tokyo Electron

July 26, 2018, Tokyo Japan – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has agreed to receive investments of about 700 million yen from Chugai Pharmaceutical Co., Ltd. (Chugai, Headquarters: Chuo-ku, Tokyo, President and CEO: Tatsuro Kosaka) and about 200 million yen from Tokyo Electron Ltd. (TEL, Headquarters: Minato-ku, Tokyo, President & CEO: Toshiki Kawai) through its subsidiary in August 2018.

PFN will use the raised capital to strengthen its financial base, improve computing resources, and continue recruiting talented people.

PFN and Chugai have established a comprehensive partnership to develop innovative drugs and services that create new value. PFN and Chugai will collaborate on joint projects to solve open problems in drug research and development as well as those for more exploratory initiatives by utilizing deep learning technology. Also, PFN and TEL have begun joint research on applications of deep learning to such areas as optimization and automation in semiconductor manufacturing.

PFN will strive to drive innovation not only in the fields of transportation system, manufacturing, and bio/healthcare but in a broad range of business areas to increase its corporate value.

 

We have received the following comments from Chugai and TEL:

 

“The fusion of existing and new technologies such as IoT and AI will become essential in all value-chain activities including research and development in the domain of healthcare and life science. By applying PFN’s cutting-edge data analysis techniques, such as machine learning and deep learning, to Chugai’s overall business operations centering on “technology-driven drug discovery,” we are aiming to deliver innovative drugs and services that address high unmet medical needs and contribute to the medical community and human health around the world.”

Osamu Okuda
Executive Vice President of Chugai in charge of Project & Lifecycle Management (Marketing) and Corporate Planning

 

“I have high expectations that PFN and TEL will be able to fuse the world’s most advanced technologies of deep learning and chipmaking to produce an epoch-making result that leads to innovation in semiconductor manufacturing.”

Toshihiko Nishigaki
Deputy General Manager, Corporate Innovation Division, Corporate Marketing, Information Technology

 

Related link:

Chugai Enters into Comprehensive Partnership Agreement with Preferred Network
https://www.chugai-pharm.co.jp/english/news/detail/20180726153001.html

*Company names and product names written in this release are the trademarks or the registered trademarks of each company.

Preferred Networks released open source deep learning framework Chainer v4 and general-purpose array calculation library CuPy v4.

Tokyo, Japan, April 17, 2018 — Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has released v4 of Chainer™ and CuPy™, major updates of the open source deep learning framework and the general-purpose array calculation library, respectively.

This major upgrade to Chainer and CuPy incorporates the results of the latest deep learning research over the last six months. The newly released v4 is largely compatible with previous versions of Chainer.

 

Main features of Chainer and CuPy v4 include:

  • Additional functions for fast, memory-efficient training on NVIDIA(R) GPUs *1

Chainer now supports NVIDIA TensorCore to speed up convolutional operations. Loss scaling has also been implemented to alleviate the vanishing gradient problem when using half-precision floats.

  • Quick installation of CuPy

We have begun providing a binary package of CuPy to reduce the installation time from 10 minutes down to about 10 seconds.

  • Optimized for Intel(R) Architecture

An Intel Deep Learning Package (iDeep) *2 backend has been added to make training and inference on Intel CPUs faster. This delivers an 8.9-fold improvement of GoogLeNet (a neural network used for image recognition) inference speed on CPUs, according to our benchmark results*3.

  • More functions supporting second order differentiation

Enhanced support for second order differentiation, which was first introduced in v3, allows easier implementation of the latest networks and algorithms.

  • A new function to export results of training with Chainer in the Caffe format

A function to export Chainer’s computational procedure and learned weights in the Caffe format has been added as experimental. This makes it easier to use the results of training with Chainer even in an environment where Python cannot be executed. (Exporting into the ONNX format is also available via the onnx-chainer package.)

 

◆Chainer ReleaseNote: https://github.com/chainer/chainer/releases/tag/v4.0.0

◆Update Guide:https://docs.chainer.org/en/latest/upgrade.html

 

Chainer and CuPy have taken in a number of development results from external contributors. PFN will continue working with supporting companies and the OSS community to promote the development and popularization of Chainer and CuPy.

 

* 1:http://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html

* 2:NumPy-compatible library for performing general arithmetic operations in deep learning at a high speed on Intel CPUs https://github.com/intel/ideep

* 3:The results of comparison in time to process an image between when iDeep was enabled and disabled. Intel Math Kernel Library was enabled in both cases. Intel Xeon(R) CPU E5-2623 v3 was used.

 

About the Chainer Open Source Deep Learning Framework

Chainer is a Python-based deep learning framework developed mainly by PFN, which has unique features and powerful performance that allow for designing complex neural networks easily and intuitively, thanks to its “Define-by-Run” approach. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications.

Chainer incorporates the results of the latest deep learning research. With additional packages such as ChainerMN (distributed learning), ChainerRL (reinforcement learning), ChainerCV (computer vision) and through the support of Chainer development partner companies, PFN aims to promote the most advanced research and development activities of researchers and practitioners in each field. (http://chainer.org/

FANUC’s new AI functions utilizing machine learning and deep learning

Tokyo, Japan, April 16, 2018 — FANUC CORPORATION (hereinafter, FANUC) in collaboration with Preferred Networks, Inc. (hereinafter, PFN) has developed new AI functions that apply machine learning or deep learning to its FA, ROBOT, and ROBO-MACHINE products.

 

FA:AI Servo Tuning (Machine Learning)

FANUC has developed AI Feed Forward as the first to come out of its development efforts in a group of AI Servo Tuning functions that realize high-speed, high-precision, high-quality machining. It utilizes machine learning to easily tune parameters for controlling servo motors in a sophisticated manner. AI Feed Forward is a feed-forward controller based on a high-dimensional model that represents mechanical characteristics more accurately. This model has too many parameters to tune manually as has been done up to now. Therefore, machine learning is used in the process to determine parameters for this advanced feed-forward control. AI Feed Forward offers high-quality machining as it reduces mechanical vibration caused when servo motors accelerate or decelerate.

Shipment estimated to start in April 2018

 

ROBOT:AI Bin Picking (Deep Learning/FIELD System Application)

 FANUC released AI Bin Picking FIELD application with 3D object scoring function to identify suitable picking order with higher success rate. This Deep Learning based application enables FANUC Robot Bin Picking system to learn the picking order automatically, and reduces robot user’s burden of the lengthy manual setup process. Also, this function makes FANUC Robot to pick up the object with higher success rate, which had only been possible with detail parameter tuning by experienced operators. Picking success rate can be even improved by creating Deep Learning trained model for each workpiece type. 

Left: FIELD BASE Pro (with NVIDIA GPU)

Right: Picking robot system with sensor (Demo unit)

Shipment started in April 2018

 

ROBOMACHINE:AI Thermal Displacement Compensation (Machine Learning)

FANUC has developed and begun selling an AI thermal displacement compensation function for FANUC’s ROBODRILL series, following the release of the same AI function for its wire-cut electric discharge machine ROBOCUT in November last year. The second AI function is for ROBOMACHINE and utilizes machine-learning technology to predict and compensate for the thermal displacement caused by temperature fluctuations, which are detected by the thermal sensors measuring ambient temperatures as well as ROBODRILL’s temperature rise while in motion. Machining accuracy has improved by about 40%, compared with an existing function. Furthermore, the optimal placement of the thermal sensors and the effective use of thermal data enable it to continue to perform optimal compensation without interrupting machining work even if there is sensor malfunction.

ROBOCUT with the first AI thermal displacement compensation function (released on November 2017)

ROBODRILL with the second AI thermal displacement compensation function

Shipment started in March 2018 (already released)

 

Comment from Toru Nishikawa,
President & CEO of Preferred Networks

“We have been working with FANUC on the AI Bin Picking project since the commencement of our R&D alliance in 2015. I feel it is of great significance that we announce its release today as the first product to apply deep learning to robots. We will continue to bring a new value to manufacturing floors by stepping up our efforts to introduce to the market smart robots and machine tools that utilize deep learning in a broader field.”

Preferred Networks to Launch “MN-1b” Private Sector Supercomputer Adopting NVIDIA Tesla V100 32GB GPUs Will expand NTT Com Group’s multi-node GPU platform

TOKYO, JAPAN — Preferred Networks, Inc. (PFN), a provider of IoT-centric deep learning systems, NTT Communications Corporation (NTT Com), the ICT solutions and international communications business within the NTT Group, and NTT Com subsidiary NTT PC Communications Incorporated (NTT PC) announced today that PFN will launch an expanded version of its MN-1 private sector supercomputer equipped with NTT Com and NTTPC’s next-generation GPU platform by July. The new MN-1b supercomputer will adopt the NVIDIA(R) Tesla(R) V100 32GB, that was announced at GTC 2018 on March 27, 2018 (U.S. time).

PFN plans to enhance MN-1 by adding 512 NVIDIA Tesla V100 32GB GPUs and have them up and running by July, with the added GPUs having a theoretical peak performance of about 56 PetaFLOPS1, a massive 56,000 trillion floating-point operations per second, based on a mixed precision floating-point operation2 used in deep learning. This means the expansion alone will contribute to a roughly threefold increase from the current peak.

PFN expects the new supercomputer’s extra high speed and massive processing environment leveraging the latest GPUs will accelerate the real-world applications of its research and development in deep learning and related technologies and thereby strengthen PFN’s global competitiveness. NTT Com and NTT PC will build and operate the multi-node platform leveraging the latest GPUs that meets PFN’s requirements, using their knowledge of intra-GPU communication and waste heat processing.

“We are truly honored that Preferred Networks has chosen NVIDIA Tesla V100 32GB, most advanced data center GPU with 2X the memory, for its next-generation private supercomputer’s computation environment, “MN-1b”. With NTT Com Group’s experience of establishing and managing highly reliable data center services, combined with NVIDIA’s latest high-speed GPUs for deep learning, we sincerely look forward to R&D results in the fields of transportation systems, manufacturing and biotech/healthcare.”
said Masataka Osaki, Vice President of Corporate Sales and NVIDIA Japan Country Manager.

Emmy Chang, Board Director, Supermicro KK and VP of Strategic Sales, Supermicro said
“Preferred Networks is the first in the world to deploy our SuperServer(R) 4029GP-TRT2 equipped with the latest version of Intel(R) Xeon(R) Scalable processors and supporting eight NVIDIA Tesla V100 32GB GPU accelerators,” “Preferred Networks has developed the world-class private supercomputer through cooperative work with NTT Com Group, and Supermicro continues to support them with our latest innovative hardware and solutions.  We are confident that Preferred Networks will achieve new heights with its new private supercomputer.”

 

PFN will use the new MN-1b to raise the speed of its ChainerTM open source deep-learning framework and further accelerate its research and development in fields that require a huge amount of computing resources, namely transportation systems, manufacturing, bio-healthcare, and creativity.

Going forward, NTT Com expects to increasingly support the delivery of AI technologies and related platforms for advanced research and commercialized deep learning, including the AI business initiatives of PFN.

 

Related links:

Chainer:

Enterprise Cloud:

Nexcenter:

 

Notes:

1 A unit measuring computer performance. Peta is 1,000 trillion (10 to the power of 15) and FLOPS is used to count floating-point operations per second. So, 1 PetaFLOPS means that a computer is capable of performing 1,000 trillion floating-point calculations per second.

2 Mixed precision floating-point operation is a method of floating point arithmetic operations with a combination of multiple precisions.

ChainerTM is a trademark or a registered trademark of Preferred Networks, Inc. in Japan and other countries. Other company names and product names written in this release are the trademarks or the registered trademarks of each company.

 

About Preferred Networks, Inc.

Founded in March 2014 with the aim of promoting business utilization of deep learning technology focused on IoT, PFN advocates Edge Heavy Computing as a way to handle the enormous amounts of data generated by devices in a distributed and collaborative manner at the edge of the network, driving innovation in three priority business areas: transportation, manufacturing and bio/healthcare. PFN develops and provides Chainer, an open source deep learning framework. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Corporation and the National Cancer Center.

https://www.preferred-networks.jp/

 

About NTT Communications Corporation

NTT Communications provides consultancy, architecture, security and cloud services to optimize the information and communications technology (ICT) environments of enterprises. These offerings are backed by the company’s worldwide infrastructure, including the leading global tier-1 IP network, the Arcstar Universal One™ VPN network reaching over 190 countries/regions, and over 140 secure data centers worldwide. NTT Communications’ solutions leverage the global resources of NTT Group companies including Dimension Data, NTT DOCOMO and NTT DATA.
www.ntt.com | Twitter@NTT Com | Facebook@NTT Com | LinkedIn@NTT Com

 

NTT PC Communications Incorporated

NTTPC Communications Incorporated (NTTPC), established in 1985 is a subsidiary of NTT Communications, is a network service and communication solution provider in Japanese telco market, The company has been the most strategic technology company of the group throughout of years. NTTPC launched the 1st ISP service of the NTT group, so called “InfoSphere” at 1995, and also launched the 1st Internet Data Center and server hosting services of Japan so called “WebARENA” at 1997. NTTPC have always started something new in ICT market.

Preferred Networks’ private supercomputer ranked first in the Japanese industrial supercomputers TOP 500 list

Preferred Networks Launches one of Japan’s Most Powerful Private Sector Supercomputers

Preferred Networks’ automatic coloring service PaintsChainer receives the Excellence Award at the 21st Japan Media Arts Festival

TOKYO, JAPAN — PaintsChainer™, an automatic coloring service developed and provided by Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa), has received the Excellence Award of the Entertainment Division at the 21st Japan Media Arts Festival organized by Agency for Cultural Affairs, Government of Japan.

The Japan Media Arts Festival publicly honors highly creative, artistic works in four fields of media arts: Art, Entertainment, Animation, and Manga. The annual festival was first organized in 1997 and has since recognized and awarded outstanding works. This year, it has received 4,192 entries from 98 countries and regions around the world, the most in its history, and has announced a grand prize winner, four excellence award winners, and three new face award winners in each of the four categories.

PaintsChainer has drawn a huge reaction on Twitter and other social media sites the moment it was released in January 2017 as a free online service that colors black and white sketches automatically. It uses a deep learning technology to recognize faces, clothing, and other background objects on an uploaded sketch image or picture, and color them automatically or based on a specified color. Currently, it provides three different coloring models Tanpopo, Satsuki, and Canna.

https://paintschainer.preferred.tech/index_en.html

The jury cites the significance of providing an automatic coloring platform as a free web service as the main reason for giving the Excellence Award to PaintsChainer.

A commemorative photo of award winners taken at the press conference at the National Art Center, Tokyo. The fourth person from the left in the middle row is Taizan Yonetsuji.

 

PaintsChainer was developed by one of PFN engineers Taizan Yonetsuji, who has commented as follows:

It all started as my personal project, so I could also study deep learning. I feel very honored to receive such an award and am truly grateful to my senior colleagues who have taught me about deep learning, everyone at Preferred Networks who has supported the launch and operation of the service, and all the users who have said PaintsChainer was fun and enjoyed using it. We will make PaintsChainer even greater while continuing to take on other new challenges.

 

● Summary

 

● 21st Japan Media Arts Festival Exhibition of Award-winning Works

All the award-winning works that represent the media arts of the modern times will be shown to the public during the exhibition, which also includes symposia, artists’ talks, workshops, and other related events.

 

※PaintsChainer™ is the registered trademark of Preferred Networks, Inc. in Japan and other countries. Other company names and product names written in this release are the trademarks or the registered trademarks of each company.

Hakusensha and Hakuhodo DY Digital Announces the Launch of Colorized Manga Products Using PaintsChainer, a Deep Learning Coloring Technology created by Preferred Networks

Tokyo, Japan, February 6, 2018 -Hakusensha Inc. (Headquarters: Chiyoda-Ku, Tokyo; President: Kazuhiko Torishima; “Hakusensha”)and Hakuhodo DY digital Inc. (Headquarters: Minato-Ku, Tokyo; President: Akira Tsuji; “Hakuhodo DY digital”), with the cooperation of Preferred Networks, Inc. (Headquarters: Chiyoda-Ku, Tokyo; President & CEO: Toru Nishikawa, “PFN”), have started distributing and marketing color version manga products with automatic coloring using deep learning technology.

We have customized the automatic coloring service PaintsChainer *1 provided by PFN, to develop a new manga coloring model. The PaintsChainer enables users to enjoy a unique look and feel with color gradations that are only possible with automatic coloring based on deep learning.

 

  • Example of coloring using PaintsChainer

Kekkon X Renai (Akira Hagio)

  • Initial release titles
Kekkon X Renai (Akira Hagio)
Watashitachi XX Shimashita (Asuka Sora)

The initial releases have been distributed through Hakusensha e-net and other major e-book stores since as early as January 24, 2018.

 

Hakusensha have published a large number of Webcomics, such as Love Silky *2, the pioneer of monthly Web magazines, and have been involved in a large number of projects collaborating with CONPYRA *3, which is produced by Hakuhodo DY digital. This project is another product of this collaboration.

In this collaboration, PFN have developed the PaintsChainer manga coloring model, and Hakuhodo DY digital have been working on requirement definitions and specifications and directed the progress of the production. Starting with the first distribution this time, they will release other colored product versions as well.

Hakusensha, Hakuhodo DY digital, and PFN aim to further innovate manga expression and aggressively work on development of new manga production technologies using deep learning.

 

*1 PaintsChainer(R)
PaintsChainer is an online automatic coloring service developed and provided by PFN, which became a big topic on Twitter and other social media sites on its release in January 2017.
After uploading black and white pictures, it colors them automatically or as specified based on deep learning technologies. PaintsChainer provides three different coloring models Tanpopo, Satsuki, and Kanna, free of charge.
Official site: https://paintschainer.preferred.tech

*2 Love Silky
Love Silky is the monthly Web magazine distributed by Hakusensha. It features a large number of manga works for women and it distributes a new issue every third Wednesday of the month. It has been five years since the first release of Love Silky in January 2013, and currently its sixty-first issue is being released in January 2018. It has two sister magazines Love Jossie (published since July 2015), which features manga works for women, and Jossie Bunko (started publication November 2017), which specializes in novels.
Official site: http://www.hakusensha.co.jp/silky_web

*3 CONPYRA
CONPYRA is the author agent business produced by Hakuhodo DY digital.
Currently a lot of new content is generated and issued by users on the digital platform every day. CONPYRA uncovers particularly excellent works using their proprietary data analytics capabilities and produces these works collaborating with the authors/creators based on agent agreements.
In addition to agent work, CONPYRA is promoting the development of a large number of businesses focusing on novels and manga, in collaboration with a variety of publishing companies.
Official site: https://conpyra.com

 

FANUC, Hitachi, and Preferred Networks to establish a joint venture company for the development of Intelligent Edge Systems

Tokyo, Japan, January 31, 2018 — FANUC CORPORATION (TSE: 6594, “FANUC”), Hitachi, Ltd. (TSE:6501, “Hitachi”), and Preferred Networks, Inc. (“PFN”) today announced that they have reached an agreement to establish a joint venture company (the “new company”) on April 2, 2018, to develop Intelligent Edge Systems(1) that utilize artificial intelligence (AI) technologies in edge devices in the industrial and social infrastructure field. Yutaka Saito, who is currently Executive Vice President and Executive Officer at Hitachi, and who will be appointed Senior Executive Vice President at FANUC on April 1, will hold a concurrent position as CEO of the new company(2).

 

(1) Intelligent Edge Systems: Systems that use AI as an intermediary between the Cloud and edge devices such as machine tools, industrial machinery, and robots to achieve cyclic, real-time control

(2) Yutaka Saito is scheduled to resign as Executive Vice President at Hitachi on March 31.

 

In recent years, innovations using AI technologies have been expanding rapidly in a variety of fields. In the field of industrial and social infrastructures in particular, AI technologies are expected to play an important role in the part close to edge devices, for example in the case of vehicles and robots.

 

In this backdrop, FANUC, Hitachi, and PFN have agreed to undertake joint development of the world’s most advanced Intelligent Edge Systems in the industrial and social infrastructure field, and to establish a joint venture company for that purpose. The new company will develop these Intelligent Edge Systems by combining FANUC’s technologies and expertise in machine tools and robots, Hitachi’s knowledge of control technologies and other aspects of OT and IT in the front lines of manufacturing, and PFN’s deep learning and distributed computing technologies. After establishing a joint venture company, the three companies will test business potential and create business plans, and then undertake actual system development and expand application fields.

 

Through the new company’s activities, FANUC, Hitachi, and PFN will develop Intelligent Edge Systems and other next-generation control systems as part of efforts to promote collaborative creation aimed at the realization of Society 5.0(3).

 

(3) A human-centered society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space.

 

 

Comment from Yoshiharu Inaba,
Representative Member of the Board, Chairman and CEO of FANUC

“FANUC is planning to continue investing efforts into the development of FIELD systems as part of its activities targeting IoT. We believe that the activities of the joint venture company, as it strives to achieve faster, cyclic, real-time control, will have a positive effect on these development activities. As a company that has specialized in the field of factory automation, we will respond quickly and flexibly to the rapid introduction of IoT in the manufacturing industry, and continue to contribute to the growth and development of this industry in the future. We have great expectations for this joint venture company as a new phase of these activities.”

 

Comment from Toshiaki Higashihara,
President and CEO of Hitachi

“It is a great honor to work with FANUC and PFN in establishing this joint venture company. Through the Social Innovation Business, where we combine knowledge and expertise accumulated through more than 100 years of experience in OT and more than 50 years in IT, Hitachi strives to resolve issues faced by society, and achieve safer, more secure, and more comfortable lifestyles for everyone. Now, by merging the strengths of these three companies, we will develop and offer the world’s most advanced Intelligent Edge Systems, to contribute to advancements in the field of industrial and social infrastructures.”

 

Comment from Toru Nishikawa,
President & CEO of Preferred Networks

“I am very pleased that we are able to begin these new activities so soon after the announcement of our capital tie-up with Hitachi in December of last year. PFN entered a capital tie-up with FANUC in 2015, and since then, we have been working together to create innovative manufacturing bases where machine learning and deep learning technologies are used to establish intelligent links between machine tools, robots, and other devices. Now, by establishing this joint venture company, our three companies will accelerate the development and provision of innovative technologies that leverage each of our respective specialty fields, to further advance this trend on a global scale.”

 

Outline of the joint venture company (Plan)

Corporate Name Intelligent Edge System, LLC
Capital 30 million yen
Investments FANUC: 10 million yen; Hitachi: 10 million yen;

PFN: 10 million yen

CEO Yutaka Saito
Head Office 3580, Shibokusa Aza-Komanba, Oshino-mura, Minamitsuru-gun, Yamanashi Prefecture, Japan
Established April 2, 2018
Outline of Business Conceptual testing and development of Intelligent Edge Systems

 

About FANUC CORPORATION

FANUC CORPORATION, headquartered at the foot of Mt. Fuji, Japan, is the global leader and the most innovative manufacturer of FA, ROBOT and ROBOMACHINE in the world. More than 260 offices in 45 countries, FANUC provides world-class customer service and support.  Since its inception in 1972, FANUC has contributed to the automation of machine tools as a pioneer in the development of computer numerical control equipment. FANUC technology has been a leading force in a worldwide manufacturing revolution, which evolved from the automation of a single machine to the automation of entire production lines.  For more information visit: http://www.fanuc.co.jp/eindex.htm.

 

About Hitachi, Ltd.

Hitachi, Ltd. (TSE: 6501), headquartered in Tokyo, Japan, delivers innovations that answer society’s challenges. The company’s consolidated revenues for fiscal 2016 (ended March 31, 2017) totaled 9,162.2 billion yen ($81.8 billion). The Hitachi Group is a global leader in the Social Innovation Business, and it has approximately 304,000 employees worldwide. Through collaborative creation, Hitachi is providing solutions to customers in a broad range of sectors, including Power / Energy, Industry / Distribution / Water, Urban Development, and Finance / Government & Public / Healthcare. For more information on Hitachi, please visit the company’s website at http://www.hitachi.com.

 

About Preferred Networks, Inc.

Preferred Networks, Inc. (PFN, Headquarters: Tokyo, Japan, President and CEO:  Toru Nishikawa) was founded in March 2014 with the aim of promoting business utilization of deep learning technology focused on IoT. PFN advocates Edge Heavy Computing as a way to handle the enormous amounts of data generated by devices in a distributed and collaborative manner at the edge of the network, driving innovation in various fields, with a focus on three business areas: transportation, manufacturing, and bio/healthcare. PFN develops and provides Chainer(R), an open source deep learning framework. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, FANUC CORPORATION, and the National Cancer Center. https://www.preferred-networks.jp/en/

Preferred Networks raises a total of over 2 billion yen from FANUC, Hakuhodo DYHD, Hitachi, Mizuho Bank, and Mitsui & Co.

December 11, 2017, Tokyo Japan – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has agreed to allocate new shares in December 2017 to Hakuhodo DY Holdings Inc. (Headquarters: Minato-ku, Tokyo, President and CEO: Hirokazu Toda), Hitachi, Ltd. (Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toshiaki Higashihara), Mizuho Bank, Ltd. (Headquarters: Chiyoda-ku, Tokyo, President and CEO: Koji Fujiwara) and Mitsui & Co., LTD. (Headquarters: Chiyoda-ku, Tokyo, President and CEO: Tatsuo Yasunaga). PFN will receive an investment of about 500 million yen from each of them.

Similarly, FANUC CORPORATION (FANUC, Headquarters: Oshino-mura, Yamanashi Prefecture, Chairman and CEO: Yoshiharu Inaba) has also agreed to acquire additional shares of PFN for about 500 million yen. FANUC and PFN formed an R&D alliance in June 2015 and a capital alliance in August of the same year.

With these deals, PFN will complete a series of fund-raising activities since the additional investment of about 10.5 billion yen from Toyota Motor Corporation, which was announced in August 2017.

PFN will use the raised capital to strengthen its financial base, improve computing resources, and hire talented people while collaborating with each of the investing companies in respective fields for growth over the mid- to long-term.

PFN will strive to drive innovation not only in the fields of transportation system, manufacturing, and bio/healthcare but in a broad range of business areas to increase its corporate value.

 

We received comments from the companies investing in PFN.

 “FANUC will apply Preferred Networks’ cutting-edge machine-learning and deep-learning techniques to our FIELD system* and all the other products to provide the most effective production system that processes information quickly and in real time at the edges of machining and assembly lines in production sites, enabling machines to collaborate with each other in a flexible and smart way.”

Yoshiharu Inaba
Chairman and CEO of FANUC

 

*FANUC Intelligent Edge Link and Drive system is an open platform to optimize manufacturing by interconnecting various devices in production sites and is available for a variety of companies to join.

 

“I have high expectations that we can create innovative solutions and services by combining Preferred Networks’ deep learning and other machine-learning techniques and implementation capabilities, and Hakuhodo DY Group’s proprietary data and creativity in the fields of advertising, marketing, media, and content. We will, in collaboration with Preferred Networks, promote the fusion of creativity and technology for implementation in the real world and realize innovations that make life richer and more enjoyable.”

Hirokazu Toda
President and CEO of Hakuhodo DY Holdings

 

“Hitachi is utilizing leading-edge technologies, including IoT and AI, and enhancing collaborative creation with various partners in the Social Innovation Business. Taking the investment as an opportunity to combine the strengths of both companies such as Preferred Networks’ knowledge of deep learning and Hitachi’s technology and know-how in the integration of OT, IT, and products, we will begin collaborative creation towards the realization of Society 5.0.”

Yutaka Saito
Representative Executive Officer, Executive Vice President and Executive Officer of Hitachi

 

“Preferred Networks is a leading company in the industry that has a high level of AI technology. Through the new partnership with Preferred Networks, Mitsui will not only promote higher operation efficiency and higher value added of our global business assets but also accelerate Digital Transformation activities that lead to the creation of new businesses with the aim of strengthening competitiveness.”

Nobuaki Kitamori
Representative Director, Executive Managing Officer, and Chief Digital Officer of Mitsui & Co.

 

Related link:

Hitachi Invests in Preferred Networks
http://www.hitachi.com/New/cnews/month/2017/12/171211.html

Mitsui to Invest in AI Company Preferred Networks, Inc.
https://www.mitsui.com/jp/en/release/2017/1225186_10832.html

 

 

 

◆ About Preferred Networks, Inc.

Founded in March 2014 with the aim of promoting business utilization of deep learning technology focused on IoT, PFN advocates Edge Heavy Computing as a way to handle the enormous amounts of data generated by devices in a distributed and collaborative manner at the edge of the network, driving innovation in various fields, with a focus on three business areas: transportation, manufacturing, and bio/healthcare. PFN develops and provides Chainer®, an open source deep learning framework. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, FANUC CORPORATION, and the National Cancer Center. (https://www.preferred-networks.jp/en/)

 

*Company names and product names written in this release are the trademarks or the registered trademarks of each company.

Preferred Networks’ private supercomputer ranked first in the Japanese industrial supercomputers TOP 500 list

MN-1, a private supercomputer used exclusively by Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has recorded a LINPACK  performance of about 1.39 PetaFLOPS 2.  As a result, MN-1 is ranked 12th in the world and 1st in Japan among industrial supercomputers in the TOP500 List (http://www.top500.org), which shows the most powerful supercomputers as of November 2017. When including supercomputers for research purposes, it is listed as 91st in the world and 13th in Japan.   

 

 

About PFN’s private supercomputer MN-1   

MN-1 is equipped with a Graphics Processing Unit (GPU) platform from NTT Communications Corporation (NTT Com, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Tetsuya Shoji) and NTT PC Communications Incorporated (NTTPC, Headquarters: Minato-ku, Tokyo, President and CEO: Motoo Tanaka), and contains 1,024 of NVIDIA (R) ’s Tesla (R) multi-node P100 GPUs. It utilizes Mellanox’s Infiniband interconnect to make high-speed distributed deep learning possible using ChainerMN 4, a distributed deep learning package developed by PFN.    

Using MN-1, PFN will further accelerate its research and development activities in various fields that require a huge amount of computing resources such as transportation systems, manufacturing and bio/healthcare.

 

 

 

1.  A benchmark to compare practical operation speed of computers

2.  A unit measuring computer performance. Peta is 1,000 trillion (10 to the power of 15) and FLOPS is used to count floating-point operations per second. So, 1 PetaFLOPS means that a computer is capable of performing 1,000 trillion floating-point calculations per second.

3.  Preferred Networks launches one of Japan’s most powerful private sector supercomputers
https://www.preferred-networks.jp/en/news/pr20170920

4.  Preferred Networks achieved the world’s fastest training time in deep learning
https://www.preferred-networks.jp/en/news/pr20171110

 

About Preferred Networks, Inc.

Founded in March 2014 with the aim of promoting business utilization of deep learning technology focused on IoT, PFN advocates Edge Heavy Computing as a way to handle the enormous amounts of data generated by devices in a distributed and collaborative manner at the edge of the network, driving innovation in three priority business areas: transportation, manufacturing and bio/healthcare. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, FANUC CORPORATION, and the National Cancer Center. (https://www.preferred-networks.jp/en/)

 

*Chainer (R)  is the trademark or the registered trademark of Preferred Networks, Inc. in Japan and other countries. Other company names and product names written in this release are the trademarks or the registered trademarks of each company.

Preferred Networks achieved the world’s fastest training time in deep learning, completed training on ImageNet in 15 minutes,using the distributed learning package ChainerMN and a large-scale parallel computer

November 10, 2017, Tokyo – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has achieved the world’s fastest training time in deep learning by using its large-scale parallel computer MN-1 1.

With the size of training data and the number of parameters expanding for the sake of better accuracy of deep learning models, computation time is also increasing. It is not unusual to take several weeks to train a model. Getting multiple GPUs to link with one another for faster training is very important to reduce the time spent on trial and error and verification of new ideas, and produce results quickly.

On the other hand, it is generally known in parallel/distributed learning that the accuracy and learning rate of a model decrease gradually with increased GPUs, due to larger batch sizes and GPU communication overhead.

This time, we have improved learning algorithms and parallel performance to address these issues, and used one of Japan’s most powerful parallel computers with 1,024 of NVIDIA(R)’s Tesla(R) multi-node P100 GPUs and leverages Chainer’s distributed learning package ChainerMN 2 for training.

As a result, we completed training ResNet-50 3 for image classification on the ImageNet 4 dataset in 15 minutes, which is a significant improvement from the previously best known result 5.

The research paper on this achievement is available in the following URL under the title “Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes”. (https://www.preferred-networks.jp/docs/imagenet_in_15min.pdf)

Based on this research result, PFN will further accelerate its research and development activities in the fields of transportation systems, manufacturing, and bio/healthcare, which require large-scale deep learning.

 

1 One of the most powerful private supercomputer in Japan, contains 1,024 of NVIDIA(R)’s Tesla(R) multi-node P100 GPUs.https://www.preferred-networks.jp/en/news/pr20170920

2 A package adding distributed learning functionality with multiple GPUs to the open source deep learning framework Chainer

3 A network frequently used in the field of image recognition

4 A dataset widely used for image classification

5 Training completed in 31 minutes using Intel(R) Xeon(R) Platinum 8160 x 1,600(Y. You et al. ImageNet Training in Minutes. CoRR,abs/1709.05011, 2017)

 

■ About the Chainer Open Source Deep Learning Framework

Chainer is a Python-based deep learning framework being developed mainly by PFN, which has unique features and powerful performance that allow for designing complex neural networks easily and intuitively, thanks to its “Define-by-Run” approach. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications.

Chainer incorporates the results of the latest deep learning research. With additional packages such as ChainerMN (distributed learning), ChainerRL (reinforcement learning), ChainerCV (computer vision) and through the support of Chainer development partner companies, PFN aims to promote the most advanced research and development activities of researchers and practitioners in each field. (http://chainer.org/

■ About Preferred Networks, Inc.

Founded in March 2014 with the aim of promoting business utilization of deep learning technology focused on IoT, PFN advocates Edge Heavy Computing as a way to handle the enormous amounts of data generated by devices in a distributed and collaborative manner at the edge of the network, driving innovation in three priority business areas: transportation, manufacturing and bio/healthcare. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Corporation, and the National Cancer Center. (https://www.preferred-networks.jp/en/)

*Chainer(R) is the trademark or the registered trademark of Preferred Networks, Inc. in Japan and other countries.

 

 

Preventive Maintenance Feature of Injection Molding Machine Using AI (Deep Learning)

FANUC CORPORATION (hereinafter, FANUC) and Preferred Networks, Inc. (hereinafter, PFN) have jointly developed AI Backflow Monitor that performs preventive maintenance on FANUC’s electric injection molding machine ROBOSHOT α-SiA series. This is the latest example of our joint initiative to apply deep learning to machine tools.

AI Backflow Monitor uses deep learning to evaluate and predict the wear state of ROBOSHOTs consumable parts (non-return valve) to let operators know before a part starts to malfunction. A conventional method requires that operators visually check waveform data for shape changes that indicate backflow of resin to assess the wear and estimate the replacement timing of the valve. The new feature utilizes the deep learning techniques to effectively analyze the waveform and digitize the wear amount, enabling it to notify operators of the best timing to replace the valve.

Additionally, AI Backflow Monitor takes advantage of its Edge Heavy feature to process data mainly on ROBOSHOT-LINKi, not in the cloud.

AI Backflow Monitor will be provided as an optional feature that can improve the operating rate of ROBOSHOT through preventive maintenance. (FANUC plans to begin taking orders in January next year.)

ROBOSHOT with this new feature will be exhibited at International Plastic Fair 2017, which will be held in Makuhari Messe from Oct. 24-28.

FANUC and PFN will continue to work together and make steady progress, step by step, towards realizing innovative and advanced manufacturing fields using AI.

ROBOSHOT α-SiA series: http://www.fanuc.co.jp/en/product/roboshot/index.html

High-level system structure

AI (Machine Learning) Improves Wire-cut EDM Accuracy

FANUC CORPORATION (hereinafter, FANUC) in collaboration with Preferred Networks, Inc. (hereinafter, PFN) has developed an AI thermal displacement compensation function which will improve the machining accuracy of ROBOCUT α-CiB series, FANUC’s wire-cut electric discharge machine (see Note 1 below). ROBOCUT with this function will be the first product using AI since FANUC and PFN began collaborating.

 

FANUC and PFN formed an R&D alliance*1 in June 2015, followed by a capital alliance*2 in August of the same year to promote a joint development of AI functions for the manufacturing industry that can efficiently improve the performance and operation rates of FANUC products. The newly developed function utilizes machine-learning (ML) technology to predict and control the variable machining accuracy caused by ROBOCUT’s temperature fluctuations, with 30% more accurate compensation than existing method. The new function is applicable from small to large workpieces.

The AI thermal displacement compensation function will be provided as an optional function to ROBOCUT, and FANUC plans to start accepting orders in November of this year. FANUC will also display the ROBOCUT with this new function at Mechatronics Technology Japan, which will be held in Port Messe Nagoya on Oct. 18-21, 2017.

ROBOCUT α-CiB series

FANUC is also developing a similar function for the ROBODRILL series that utilizes ML and expect to release it in the near future.
FANUC and PFN will continue making gradual but steady progress towards realizing innovative manufacturing fields through AI.

“It is my pleasure to announce our first product based on the machine-learning technology since the tie-up with FANUC. Through this product, we can demonstrate using ML is effective in optimizing control parameters, which is one of the most important issues facing the manufacturing industry. PFN will continue to contribute to the intelligence of machine tools and robots by applying machine learning and deep learning techniques.”
Toru Nishikawa, Chief Executive Officer of PFN

 

*1 Announcement for R&D alliance with FANUC Corporation
https://www.preferred-networks.jp/en/news/8731
*2 Announcement for capital tie-up between FANUC and PFN
http://www.fanuc.co.jp/en/profile/pr/newsrelease/notice20150821.html

 

Note 1. Wire-cut EDM is a precision and fine shape machining tool that uses discharge phenomenon between the ultrathin wire electrode and the metal workpiece (electric conductor).

 

Preferred Networks Launches one of Japan’s Most Powerful Private Sector Supercomputers

Features NTT Com Group’s Cloud-based GPU platform

TOKYO, JAPAN — This September, Preferred Networks, Inc. (PFN), a provider of IoT-centric deep learning systems, NTT Communications Corporation (NTT Com), the ICT solutions and international communications business within the NTT Group, and NTT Com subsidiary NTT PC Communications Incorporated (NTTPC) announced today the launch of a private supercomputer designed to facilitate research and development of deep learning, including autonomous driving and cancer diagnosis.

The new supercomputer is one of the most powerful to be developed by the private sector in Japan and is equipped with NTT Com and NTTPC’s Graphics Processing Unit (GPU) platform, and contains 1,024 of NVIDIA(R)’s Tesla(R) multi-node P100 GPUs. Theoretically, the processing speed of the new supercomputer can reach 4.7 PetaFLOPS—a massive 4,700 trillion floating point operations per second—the fastest levels of any computing environment in Japan.

Overview of the private supercomputer

 

PFN’s deep learning research demands an ultra high-speed, high capacity, state-of-the-art computing environment. Existing GPU platforms require massive electricity supplies, generate excessive heat and offer inadequate network speed. To address these issues, PFN adopted the NTT Com Group’s proven GPU platform, which boasts significantly advanced technology. They additionally leveraged the latest data center design, building a large-scale multi-node platform using ChainerMN, PFN’s technology that significantly accelerates the speed of deep learning by parallelizing calculations over multiple nodes.

NTT Com group has developed and released a multi-node GPU platform on Enterprise Cloud, Nexcenter(TM), a world-leading data center service, which incorporates the group’s extensive know-how in GPU performance maximization.

Following the supercomputer launch, PFN plans to increase the processing speed of its open source deep learning framework Chainer. They will further accelerate their research and development in the field of transportation systems, manufacturing, bio and healthcare industry which require a huge amount of computing resources. PFN will additionally consider the deployment of NVIDIA(R) Tesla(R) V100 GPUs, which are based on next-generation Volta GPU technology. NTT Com group will continue to support PFN’s research and the commercialization of their developed solutions with AI-related technologies and platforms.

“NVIDIA is excited to see the launch of Preferred Networks’ private supercomputer, built in partnership with NTT Com Group. Computing power is the source of competitive advantage for deep learning, the core technology of modern AI. We have high expectations that the new system will accelerate Preferred Networks’ business and contribute to Japan’s economic growth.”

Masataka Osaki
NVIDIA Japan Country Manager, Vice President of Corporate Sales

 

Related links:

Chainer
Enterprise Cloud
Nexcenter

 

◆ About Preferred Networks, Inc.
Founded in March 2014 with the aim of promoting business utilization of deep learning technology focused on IoT, PFN advocates Edge Heavy Computing as a way to handle the enormous amounts of data generated by devices in a distributed and collaborative manner at the edge of the network, driving innovation in three priority business areas: transportation, manufacturing and bio/healthcare. PFN develops and provides Chainer, an open source deep learning framework. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Corporation, and the National Cancer Center.
https://www.preferred-networks.jp/en/

◆ About NTT Communications Corporation
NTT Communications provides consultancy, architecture, security and cloud services to optimize the information and communications technology (ICT) environments of enterprises. These offerings are backed by the company’s worldwide infrastructure, including the leading global tier-1 IP network, the Arcstar Universal One™ VPN network reaching over 190 countries/regions, and 140 secure data centers worldwide. NTT Communications’ solutions leverage the global resources of NTT Group companies including Dimension Data, NTT DOCOMO and NTT DATA.
www.ntt.com | Twitter@NTT Com | Facebook@NTT Com | LinkedIn@NTT Com

◆ NTT PC Communications Incorporated
NTTPC Communications Incorporated (NTTPC), established in 1985 is a subsidiary of NTT Communications, is a network service and communication solution provider in Japanese telco market, the company has been the most strategic technology company of the group throughout of years. NTTPC launched the 1st ISP service of the NTT group, so called “InfoSphere” at 1995, and also launched the 1st Internet Data Center and server hosting services of Japan so called “WebARENA” at 1997. NTTPC have always started something new in ICT market.
http://www.nttpc.co.jp/english/

 

 

Notes
1. Chainer(R) is the trademark or the registered trademark of Preferred Networks, Inc. in Japan and other countries.

2. Other company names and product names written in this release are the trademarks or the registered trademarks of each company.

Preferred Networks received about 10.5 billion yen in investments from Toyota Motor Corporation

Preferred Networks received about 10.5 billion yen in investments from Toyota Motor Corporation. Accelerate joint research and development of AI technology in the mobility field

Tokyo, Japan, August 4, 2017 – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) agreed to receive an additional investment of approximately 10.5 billion yen from Toyota Motor Corporation (Toyota, Headquarters: Toyota  City, Aichi Prefecture, President: Akio Toyoda). PFN will allocate new shares to Toyota. After the allocation, Toyota will be the largest external shareholder of PFN.

PFN and Toyota started the joint research and development in October 2014, and Toyota invested 1.0 billion yen in PFN in December 2015 to strengthen the relationship.

Through the past joint research and development of object recognition technologies and vehicle information analysis, PFN’s world-leading intelligent technologies in machine learning, deep learning, and big data processing have been highly appreciated as essential for Toyota aiming at realizing the next generation of mobility society, including autonomous driving.

With this funding, PFN will enhance the computational environment and accelerate talent acquisition. We will further accelerate joint research and development by strengthening our relationship with Toyota in the field of mobility.

 

◆ About Preferred Networks, Inc. https://www.preferred-networks.jp/

Founded in March 2014 with the aim of business utilization of deep learning technology focused on IoT. PFN advocates Edge Heavy Computing as a way to handle the enormous amount of data generated by devices in a distributed and collaborative manner at the edge of the network and realizes innovation in the three priority business areas of the transportation system, manufacturing industry, and bio/healthcare.

PFN develops and provides Chainer, and open source deep learning framework, and solutions based on the Deep Intelligence in Motion (DIMo) platform that provides state-of-the-art deep learning technology. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Corporation, National Cancer  Center.

*Chainer® and DIMoTM are trademarks of Preferred Networks, Inc. in Japan and other countries.

 

Preferred Networks appoints Professor Pieter Abbeel of UC Berkeley as a Technical Advisor

Preferred Networks, Inc. (PFN, HQ: Chiyoda-ku, Tokyo, President & CEO: Toru Nishikawa) agreed with Pieter Abbeel (Professor at University of California, Berkeley, and Research Scientist at OpenAI) that he will be appointed as a technical advisor at PFN as of August 1st, 2017.

Professor Abbeel is a pioneer in machine learning based optimization and automation in robotics. He has published many research papers on robot control using deep reinforcement learning. Recently he joined OpenAI, a non-profit AI research company as research scientist.

In this appointment as a technical advisor, Professor Abbeel will give technical advice and guidance on cutting-edge deep learning to robotics research in PFN, aiming to accelerate the development and practical use of PFN’s technology. 

For more information, please refer to the research blog: https://preferredresearch.jp/2017/07/22/abbeel/

 

  • Pieter Abbeel

  • Biography

    He received a BS/MS in Electrical Engineering from KU Leuven (Belgium) and received his Ph.D. degree in Computer Science from Stanford University in 2008. He joined the faculty at UC Berkeley in Fall 2008, with an appointment in the Department of Electrical Engineering and Computer Sciences. His research focuses on robotics, machine learning and control. Professor Abbeel has won various awards, including IEEE Robotics and Automation Society Early Career Award, Sloan Research Fellowship. His work has been featured in many popular press outlets, including BBC, New York Times, and MIT Technical Review.

*DIMo™ are trademarks of Preferred Networks, Inc. in Japan and other countries.

Preferred Networks released Version 2 of Chainer, an Open Source framework for Deep Learning

New functions developed, including a significant increase of memory efficiency during learning

Tokyo, Japan, June 2, 2017 – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has released a major update of its open source deep learning framework Chainer, called Chainer v2.

This is the first major version update since the official release of Chainer in 2015, and it enables more powerful, flexible, and intuitive functions to implement and study deep learning methods.

With the rapid evolution of deep learning technology and an expanding field of target applications, user demands for the functionality of deep learning frameworks is rapidly changing and diversifying.

Chainer incorporates the results of the latest deep learning research. With additional packages such as ChainerMN (distributed learning), ChainerRL (reinforcement learning), ChainerCV (computer vision) and through the support of Chainer development partner companies, PFN aims to promote the most advanced research and development activities of researchers and practitioners in each field.

 

Chainer v2 has three major enhancements and improvements.

 

1. Improved memory efficiency during learning

Chainer v2 shows significantly reduced memory usage without sacrificing learning speed. It has been confirmed that the memory usage can be reduced by 33% or more when learning using the network ResNet50 used in the field of image recognition. This makes it easier to design larger networks and allows to learn using larger batch sizes in usual networks.

 

2. Chainer’s accompanying array library CuPy has been separated and made into an independent project, allowing a broader range of HPC applications to be easily accelerated using GPUs

The general-purpose array calculation library CuPy is highly compatible with library NumPy, which is very popular in the field of scientific computing, making it possible to run faster using the GPU without altering the code written for use with NumPy. By separating CuPy and developing it as a separate library, we aim to increase users for expanding our application not only in deep learning field but also in other research and development fields.

 

3. Organized the API and made it more intuitive

One of the major features of Chainer is its ability to intuitively describe a complex neural network as a program. We have taken into consideration the various use cases and needs of the community to remove unnecessary options and organize interfaces to provide a more sophisticated API. Due to a more intuitive description, unintentional bugs occur less frequently.

 

● Chainer Release Note: https://github.com/chainer/chainer/releases/tag/v2.0.0

● Chainer Upgrade Guide: https://docs.chainer.org/en/stable/upgrade.html

● Chainer Blog: https://chainer.org/announcement/2017/06/01/released-v2.html

 

The Chainer team plans to release major version updates every four months to support the most advanced research and development activities for researchers and practitioners in each field.

Development results of many external contributors are also included in the Chainer V2 release. PFN will continue to work with support companies and the OSS community to promote the development and dissemination of Chainer.

 

◆ Chainer Meetup # 05

Community event for developers and researchers who use Chainer.

  • Date: June 10, 2017, 14:00–18:30
  • Place: Microsoft Japan Co., Ltd. Shinagawa Office, Seminar Room A
    (Shinagawa Grand Central Tower 31f, 2-16-3 Konan, Minato-ku, Tokyo)
  • Application: https://chainer.connpass.com/event/57307/

 

◆ Kick-off for the Deep Learning Lab Community

The Deep Learning Lab is a community of professionals who are well versed in both technology and business to apply the latest deep learning technology to real business. Microsoft Azure and Chainer are used in key platforms/frameworks to disseminate information about use examples and the latest technology trends.

  • Date: Monday, June 19, 2017, 9:00-12:30
  • Place: Microsoft Japan Co., Ltd. Shinagawa Office
    (Shinagawa Grand Central Tower 31F, 2-16-3 Konan, Minato-ku, Tokyo)
  • Application: https://dllab.connpass.com/event/57981/

 

◆ About the Chainer Open Source Deep Learning Framework (http://chainer.org)

Chainer is a Python-based deep learning framework developed by PFN, which has unique features and powerful performance that enables users to easily and intuitively design complex neural networks, thanks to its “Define-by-Run” approach. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications.

*Chainer(R) and DIMo(TM) are trademarks of Preferred Networks, Inc. in Japan and other countries.

Drawing app “pixiv Sketch” and automatic coloring service “PaintsChainer” collaborate to provide a new function for automatic coloring of illustrations!

Artificial Intelligence (AI) supports the “coloring” of sketches and illustrations by providing new functions to recognize faces, clothes, and background in the image and automatically filling them with color and shading.

Tokyo, Japan, 24 May 2017 – pixiv Inc. (President: Hiroki Ito, Headquarters: Shibuya-ku, Tokyo) and AI startup Preferred Networks, Inc.  (President & CEO: Toru Nishikawa, Headquarters: Chiyoda-ku, Tokyo, hereinafter referred to as PFN) collaborate to add the new function of automatic coloring, realized by “PaintsChainer”, to the drawing communication platform “pixivSketch”, available from Wednesday, May 24, 2017.

pixiv Sketch is a communication platform that allows users to post drawings directly from devices such as PCs and smartphones. Even when relaxing or playing outside with friends, users can paint anytime and anywhere and experience communication in real-time by posting and sharing their drawings.

The new functionality added to pixiv Sketch is realized using the technology of PaintsChainer that can automatically select painting colors, trained from pairs of line drawings and colored illustrations using Chainer, a deep learning framework developed and provided by PFN.

It allows the user to perform the important process of “coloring” when producing illustrations by selecting a picture drawn on pixiv Sketch or an external image file and then simply clicking the automatic coloring button. Face, clothing, and the background of the illustration are recognized by AI and colors are automatically added. You can also put your favorite color chosen from a color palette as a hint for automatic coloring at any point on the line drawing.

pixiv and PFN will continue to provide valuable services to make drawing and painting more natural and pleasant through AI technology and research.

◆ Automatic coloring function in pixiv Sketch

Release date: May 24th

Cost: free

URL: https://sketch.pixiv.net/ (Available only on Web version)

How to use the new function;

1. Draw a line drawing or select an image of a line drawing

2. Start the automatic coloring tool by pressing the “Automatic coloring” button

3. Select the coloring pattern of your choice from two different styles

4. If necessary, put color hints from the color palette to adjust the coloring

5. After specifying the colors, click the arrow button to complete the coloring process!

 

◆ pixiv Sketch  https://sketch.pixiv.net/

pixiv Sketch is a painting communication platform launched with the desire to “make everyday paintings more casual and fun”. It is a service where you can post images you’ve painted anytime, anywhere through devices such as PCs and smartphones.

 

PaintsChainer   https://paintschainer.preferred.tech/

PaintsChainer is developed and offered by PFN, and received had a great response on Twitter and other social media sites when the service was released in January 2017. Users can upload a black and white drawing file and have it colored automatically using deep learning technology. The user can also supply color hints to control the colorization results.

 

◆ About Preferred Networks, Inc. https://www.preferred-networks.jp/

Founded in March 2014 with the aim of business utilization of deep learning technology focused on IoT. PFN advocates Edge Heavy Computing as a way to handle the enormous amount of data generated by devices in a distributed and collaborative manner at the edge of the network and realizes innovation in the three priority business areas of the transportation system, manufacturing industry, and bio/healthcare.

PFN develops and provides solutions based on the Deep Intelligence in Motion (DIMo) platform that provides state-of-the-art deep learning technology. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Inc., National Cancer Research Center.

Preferred Networks and Microsoft have a strategic collaboration in the field of deep learning solutions

Tokyo, Japan, 23 May 2017 – Today, Preferred Networks, Inc. (Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa, hereinafter referred to as PFN) and Microsoft Corporation (Headquarters: Redmond, Washington, USA, CEO: Satya Nadella) have agreed to strategically collaborate in the field of deep learning solutions with the aim to accelerate the applications of artificial intelligence and deep learning in the business world.

Based on this alliance, both companies will promote cooperation between Microsoft’s public cloud platform Microsoft Azure and PFN’s deep learning technology to provide deep learning solutions for solving problems across a broad range of industries. Microsoft Corporation (Headquarters: Minato-ku, Tokyo, Representative Director: Takuya Hirano) will fully support the delivery of this collaboration to the Japanese market.

 

Through this collaboration, both companies will work together in the following three areas;
1) Technology, 2) Human resource development, 3) Marketing.

1.Technology:

  • Challenges that engineers face in deep learning include the increase of the time required to train complex neural networks, the growing management complexity associated with ever-increasing data, to remain flexible and adaptable to the rapid progress and innovation of deep learning, and the methodology of system development around deep learning. In this collaboration, with the aim of tackling these challenges, both companies will enhance the compatibility between Microsoft Azure IaaS and PFN’s deep learning framework Chainer, providing an Azure template to deploy Chainer/ChainerMN (MN stands for Multi Node) on Azure IaaS with a single click, Chainer to Data Science VMS, Chainer on Azure batch services and SQL Servers, and improving Chainer on Windows by the summer of 2017.

 

  • Currently, the standard way of developing neural networks is to develop from scratch. However, it needs high technical knowledge, and the amount of required investment is also very large. In order to drive the application of deep learning to the real world, it is essential to move from development from scratch to standardized solutions. To realize this transfer, Microsoft Azure Data + Analytics products and PFN’s deep learning platform, Deep Intelligence in-Motion (DIMo) are combined to provide solutions for specific workloads and industries throughout 2017. In addition, both companies will support and nurture partnerships in the development of these solutions to accelerate the broader implementation in the real world.

 

2.Human resource development:

  • The development of data science human resources is one of the main issues of applying deep learning to the real world. In order to resolve this issue, both companies will work together to provide training programs for university students, engineers and researchers throughout 2017. In addition, both companies will consider participation in data science related programs for human resource development, which are typically government organized, for higher education institutions.

 

  • Training programs include not only the basics of neural networks, but also advanced classes that teach how to actually apply deep learning to real-world business applications. Through these training programs, both companies plan to train 50,000 people in three years. As goals for the training, programs such as Imagine Cup and Azure for Research, which are among the world’s largest IT contests for students aiming for fostering international competitive IT talents are considered.

 

3.Marketing:

  • Deep learning is just one method in machine learning, but it is now exposed to many people as a related field of artificial intelligence. As a result, it is difficult for customers to determine whether or not deep learning is effective to solve their business problems. Both companies will start a customer workshop for each industry in the summer of 2017 based on the knowledge of deep learning business cultivated by Microsoft and PFN, and real success stories using Microsoft Azure, Chainer and DIMo.

 

  • By incorporating the latest deep learning technologies provided by Chainer and DIMo on a solid Azure platform, both companies provide an enterprise-grade end-to-end solution that can be incorporated into the customer’s core system, throughout 2017.

 

  • As a place of matching between customers who want to solve business problems with deep learning, and companies who provide consulting services and system development using deep learning, a community named “Deep Learning Lab” has been established, and the community will hold briefings on the days of June 19 and July 25, 2017.
    https://dllab.connpass.com/

Preferred Networks released ChainerMN, a multi-node extension to Chainer, an open source framework for deep learning

Tokyo, Japan, 9 May 2017 –

Today, Preferred Networks, Inc. (Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa, hereinafter PFN)  released ChainerMN (MN stands for Multi-Node, https://github.com/pfnet/chainermn), which can accelerate the training speed by adding a distributed learning function with multiple GPUs to Chainer, the open source deep learning framework developed by PFN.

Even though the performance of GPUs is continuously improving, the ever-increasing complexity of neural network models, with large number of parameters and much larger training datasets requires more and more computational power to train these models. Today, it is common that one training session takes more than a week on a single node of a state-of-the-art computer.

Aiming to provide researchers with an efficient way to conduct flexible trial and error iterations, while using large training data sets PFN developed ChainerMN, a multi-node extension for high-performance distributed training, built on top of Chainer. We demonstrated that ChainerMN finished training a model in about 4.4 hours with 32 nodes and 128 GPUs which would require about 20 days on a single-node, single GPU machine.

 

  • Performance comparison experiment between ChainerMN and other frameworks

https://research.preferred.jp/2017/02/chainermn-benchmark-results/

We compared the performance benchmark result of ChainerMN with those of other popular multi-node frameworks. In our 128-node experiments with a practical setting, in which the accuracy is not sacrificed too much for speed, ChainerMN outperformed other frameworks.

 

When comparing the scalability, although the single-GPU throughputs of MXNet and CNTK (both are written in C++) are higher than ChainerMN (written in Python), we found that the throughput of ChainerMN was the highest with 128 GPUs, showing that ChainerMN is the most scalable. This result was due to the design of ChainerMN which is optimized for both intra-node and inter-node communications.

 

Existing Chainer users can easily benefit from the performance and scalability of ChainerMN simply by changing a few lines of their original training code.

ChainerMN has already been used in multiple projects in a variety of fields such as natural language processing and reinforcement learning.

 

  • About the open source deep learning framework Chainer

Chainer is a Python-based deep learning framework developed by PFN, which has unique features and powerful performance that enables users to easily and intuitively design complex neural networks thanks to its “Define-by-Run” feature. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications. (http://chainer.org/

 

  • About Preferred Networks, Inc.

Founded in March 2014 with the aim of business utilization of deep learning technology focused on IoT. Edge Heavy Computing handles the enormous amount of data generated by devices in a distributed and collaborative manner at the edge of the network and realizes innovation in the three priority business areas of the transportation system, manufacturing industry, and bio- healthcare.

PFN develops and provides solutions based on the Deep Intelligence in-Motion (DIMo, Daimo) platform that provides state-of-the-art deep learning technology. Collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Inc., National Cancer Research Center,  we are promoting advanced initiatives.(https://www.preferred-networks.jp/en/

 

*Chainer(R) and DIMo(TM) are a trademark of Preferred Networks, Inc. in Japan and other countries.

Intel and Preferred Networks collaborate to jointly develop Chainer, deep learning open source framework

The companies aim to significantly accelerate CPU performance for Chainer running on Intel Architecture.

Intel Corporation and Preferred Networks Inc. (PFN) announced today that the companies will collaborate on the development of Chainer(R)(http://chainer.org/), PFN’s open source framework for deep learning, with the aim to accelerate out of the box deep learning performance on general purpose infrastructure powered by Intel.

 

Advanced technologies including IoT (Internet of Things), 5G (fifth generation mobile networks), and AI (artificial intelligence) are expected to be used in a range of industries ahead, giving rise to data-driven business opportunities and user experiences. The advance of technologies related to AI and deep learning, in particular, will accelerate the creation of applications that further enhance the intrinsic value of data.

The use of special-purpose computing environments for developing and implementing AI applications and deep learning frameworks poses challenges for the developer community, including development complexity, time and cost.

PFN, the developer of Chainer, which is an advanced deep learning framework with a reputation for ease of use among application developers in various industries, and Intel Corporation, a provider of general purpose computing technologies and industry leading AI/deep learning accelerators, will collaborate in an effort to make AI development easier and more affordable. The collaboration will bring both companies’ technologies to bear in the aim of optimizing development/execution of applications that use advanced AI and deep learning frameworks, as well as accelerating the performance of image and voice recognitions.

Chainer is a Python-based deep learning framework developed by PFN, which has unique features and powerful performance that enables users to easily and intuitively design complex neural networks thanks to its “Define-by-Run” feature. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications.

Intel Corporation, a technology leader that is uniquely poised to drive the AI computing era, will help Chainer deliver breakthrough deep learning throughput for the industry’s most comprehensive compute portfolio for AI, which includes the Intel(R) Xeon(R) processors, Intel(R) Xeon Phi™ processors, Intel(R) Arria(R) 10 FPGAs, Intel(R) Nervana™ technology and more products. This framework will employ the highly-optimized Intel’s open source library—Intel(R) Math Kernel Library (MKL) and Intel(R) Math Kernel Library Deep Neural Network (MKL-DNN) as a fundamental building block.

 

Through the collaboration, Intel and PFN will undertake the following.

  •  Continuously optimize the performance of Chainer on Intel architecture
  •  Continuously align to Chainer updates
  •  Continuously optimize Chainer to updates to Intel architectures for general purpose computing, accelerators, libraries, and so on
  •  Share the results of the companies’ collaboration with the community on Intel’s GitHub repository
  •  Collaborate on marketing activities designed to accelerate AI/deep learning market growth

 

*Intel, Xeon, Xeon Phi, Arria and Nervana are trademarks or registered trademarks of Intel Corporation in the United States and other countries.

*Chainer, DIMo are trademarks or registered trademarks of Preferred Networks, Inc. in Japna and other countries.

SORACOM and Preferred Networks carry out Co-demonstration of “Edge Heavy Computing” using machine learning technology on IoT equipment at “CeBIT 2017” in Hannover Germany

Preferred Networks, Inc. (Headquarters: Chiyoda-ku, Tokyo; President and CEO: Toru Nishikawa, hereinafter PFN), and SORACOM,INC(Headquarters: Setagaya Ward, Tokyo; President and CEO: Ken Tamagawa), announce that at the CeBIT 2017 International Information and Communication Technology Trade Fair, to be held in Hanover Germany from 20th to 24th March, we will carry out a joint demonstration using the deep learning technology developed by PFN on IoT equipment connected to Soracom’s network to showcase the concept of “Edge-Heavy Computing.”

The “SORACOM” IoT communications platform delivers secure, scalable over-the-air connectivity purpose-built for IoT, and supports rapid deployment and operation of IoT systems. With the cloud-native “SORACOM” platform, IoT connectivity can be effectively secured from end to end, integrated natively with leading cloud services, and managed directly via Web console or API.

PFN has strengths in machine learning technologies, including IoT and deep learning. PFN develops and provides software platform products that enable data analytics in distributed and collaborative manner to realize advanced intelligence in the fields of manufacturing, transportation, and bio-healthcare.

With wireless technology such as cellular line provided by Soracom, it is becoming easier to send data directly from the IoT devices to the cloud. However, considering bandwidth and data volume, there is still a challenge to continuously transmit large-scale data such as video and audio in real time.

The demonstration of this time is to make it possible to solve these problems by executing deep learning technology and analyze the data directly on the IoT device, transmitting only the truly valuable data to the cloud. PFN calls this technology “edge heavy computing.” By delivering real-time analysis at the edge, it is possible to solve challenges related to privacy and bandwidth, minimizing data volume required for transmission and discarding video data immediately after analysis.

Co-demo of “Edge Heavy Computing” Details:

1. Content of the demo
We will demonstrate PFN’s deep learning platform “DIMo (Deep Intelligence in-Motion)” operating on “NVIDIA® Jetson™ TX 1”, a high-performance embedded GPU module. Then, we will analyze the demographics of individuals observed by a camera connected to “NVIDIA Jetson TX 1” on the spot. After that, we will forward only the summarized information to the cloud using “SORACOM Air,” and visualize it using “SORACOM Harvest”.

2. Benefits of “Edge Heavy Computing”

The merits obtained by transferring only rough information such as age and gender of the person captured by the camera and position information in the image to the cloud are as follows:

      ●When sending images to the cloud directly, resolution and quality have to be dropped, increasing the difficulty of analysis

 

      ●Compared to the typical case of sending images directly to the cloud for analysis, it is only necessary to send very small amounts of data

 

      ●Analysis of high quality and high resolution video without can be conducted without concern for data size

 

      ●Since images are not accumulated in the cloud, they are easily discarded on the camera side after analysis and privacy can be easily maintained

 

3. Exhibit of joint demonstration Details

・Time: 20th – 24th March 2017
・Venue: Hannover, Germany “CeBIT 2017 (International Information and Communication Technology Trade Show)”
・Booth number: Hall 12, Stand B 37 http://www.cebit.de/exhibitor/soracom-dk/U177198

* Soracom will also exhibit in Japan Pavillion, Hall 4, Stand A 38, but the above demo exhibition will be Hall 12 only.

 

NVIDIA offered edge devices for this demonstration exhibition of “Edge Heavy Computing”

Mr. Masataka Osaki, NV representative, Japan representative and vice president of US head office

“As an AI computing company, NVIDIA offers end-to-end solutions from learning server side to deep learning to edge side inference.” The demonstration of this “edge heavy computing” will embody the deep learning solution and will continue to support the efforts of both companies.”

demo1

demo2

About SORACOM,INC
Soracom Corporation provides a communication platform, “SORACOM,” for IoT. This platform, launched in September 2015, is a mobile communication service specialized for IoT. By using SORACOM, customers can incorporate communication essential to the IoT system reasonably, securely and programmably into the system.

About Preferred Networks, Inc.
Founded in March 2014 with the aim of business utilization of deep learning technology focused on IoT. Edge Heavy Computing handles the enormous amount of data generated by devices in a distributed and collaborative manner at the edge of the network and realizes innovation in the three priority business areas of the transportation system, manufacturing industry, and bio- healthcare.

PFN develops and provides solutions based on the Deep Intelligence in-Motion (DIMo, Daimo) platform that provides state-of-the-art deep learning technology. Collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Inc., National Cancer Research Center, we are promoting advanced initiatives.

About NVIDIA Jetson TX 1
Jetson TX 1 is a supercomputer installed in a credit card size module. NVIDIA Maxwell ™ architecture, 256 NVIDIA CUDA® core, 64-bit CPU with very efficient processing power. It also has the latest technology of deep learning, computer vision, GPU computing and graphics, making it the most suitable module for embedded visual computing.

NVIDIA Jetson TX 1

PFN recognized as one of most innovative and exciting Japanese startups at 2016 US-Japan Innovation Awards

Toru Nishikawa, CEO, receives “Innovation Showcase” award at the symposium

Preferred Networks recognized as one of most innovative and exciting Japanese startups at 2016 US-Japan Innovation Awards

Palo Alto, CA — July 22nd, 2016 — Preferred Networks, Inc. (PFN) was selected one of the five “Innovation Showcase” companies at 2016 US-Japan Innovation Awards Symposium held in the Stanford University Arrillaga Alumni Center.

US-Japan Innovation Awards is operated by the Japan Society of Northern California in collaboration with the Stanford University US–Asia Technology Management Center. The “Innovation Showcase” is given to most exciting Japanese startup companies that have potential to disruput or transformational globally and to be known to the Sillicon Valley.

 

About Preferred Networks

Preferred Networks Inc. (PFN) is a Tokyo-based startup focusing on applications of latest artificial intelligence technologies to emerging problems in the Internet of Things (IoT). PFN’s vision is the realization of Deep Intelligence – a future IoT in which all devices, as well as the network itself, are intelligent. PFN develops software related to deep learning and IoT, and provides Deep Intelligence in-Motion solutions. PFN collaborates with many world-leading companies in industries, such as FANUC for intelligent robots and Toyota motors for autonomous driving. For more information please visit: www.preferred-networks.jp/en

Manufacturing Automation Leaders Collaborate: Optimizing Industrial Production through Analytics

TOKYO, — April 18, 2016 —FANUC CORPORATION, the world’s leading supplier of robotics and factory automation, is collaborating with Cisco, the worldwide leader in IT-enabled digitization, as well as Rockwell Automation, the world’s largest company dedicated to industrial automation and information solutions, and Preferred Networks, a leading provider of Artificial Intelligence solutions. The companies will work together on the development and deployment of the FANUC Intelligent Edge Link and Drive (FIELD) system, a platform that connects not only Computer Numerical Control machines (CNCs) and robots, but also peripheral devices and sensors – to deliver analytics that optimize manufacturing production.

The FIELD system will be a platform for the delivery of advanced analytics for FANUC CNCs, robots, peripheral devices, and sensors used in automation systems. It will drive improved machine reliability, quality, flexibility, and speed – elevating the Overall Equipment Efficiency (OEE) and increasing manufacturing profitability. It will also provide advanced machine learning and deep learning capabilities. By working with Cisco, Rockwell Automation, and Preferred Networks – FANUC will offer a complete solution including network and compute infrastructure, applications, and an enabling middleware platform. With this open platform, application developers, sensor and peripheral device makers, system integrators and others can build solutions that improve equipment efficiency, manufacturing output, and quality.

Extension of Current Successes
The FIELD system extends the success of the existing FANUC ZDT (Zero Downtime) connected robots project; ZDT is built with Cisco cloud, IOT data collection software, and end to end security capabilities. The companies are working together to implement systems for major automotive manufacturers by leveraging the Allen-Bradley Stratix Ethernet switches from Rockwell Automation to connect robots to a Cisco Unified Computing System (UCS) – all of this running on FANUC and Cisco’s ZDT Data Collection software. Automotive customers who have implemented this system are quickly realizing a significant decrease in production downtime as well as increased cost savings.

Intelligent Manufacturing
The FIELD system will bring the power of advanced machine learning and artificial intelligence into the hands of customers and application developers — driving manufacturing to a new level of productivity and efficiency. FANUC and Preferred Networks have established these new technologies for applications such as Bin Picking robots, anomaly detection, and failure prediction. The FIELD system combines both artificial intelligence and edge-computing technologies – enabling distributed learning. Data is generated from robots and machines and is processed in real time at the edge of the network. This allows those devices to intelligently coordinate and collaborate in a flexible manner – resulting in sophisticated manufacturing practices not before possible.

 

Who provides What for the FIELD System?

FANUC: Provides CNCs and robots with embedded sensors to track key variables required to improve machine reliability, quality, and speed.

FANUC, Cisco and Preferred Networks: Provides enabling middleware Platform Software, as well as security and application lifecycle management applications. It will also leverage Preferred Networks’ open deep learning framework (Chainer), IoT Stream Engine (SensorBee) – and other advanced machine learning libraries within its Deep Intelligence in-Motion (DIMo) platform.

Cisco and Rockwell Automation: Provides networking, compute and security infrastructure to connect the robots, CNCs, and other cell equipment to the FIELD applications. Based on the Rockwell Automation and Cisco collaboratively developed Converged Plantwide Ethernet (CPwE) architecture – this further drives improved security, connectivity, flexibility, and scalability – all of which allows connection from a single, small cell to large factory with hundreds of cells.

FANUC, Rockwell Automation and Preferred Networks: Delivers the initial Application Software on top of the FIELD middleware and infrastructure platform. This will extend the LINK-i and ZDT applications currently deployed, along with additional deep learning applications from the FANUC and Preferred Networks partnership. It is planned that Rockwell Automation manufacturing software products (including FactoryTalk View, FactoryTalk VantagePoint, and FactoryTalk Production Center) will seamlessly integrate with the FIELD system to speed deployment.

 

LEADERSHIP QUOTES

Sujeet Chand, Senior VP and CTO, Rockwell Automation
“By collaborating with world class companies, Rockwell Automation helps maximize manufacturers’ investments by leveraging the data from their intelligent devices they are using today to drive an enterprise-wide analytics strategy. With a secure scalable compute approach to analyze this data – from device to the enterprise – users can improve operations and make more informed decisions tailored to meet the needs of their organizations.”

Rowan Trollope, SVP IoT and Applications, Cisco
This collaboration represents a historic shift in the industry, with IoT, industrial automation and machine learning coming together to make the factory of the future a reality. It’s been talked about for years, but now it is really happening. Cisco couldn’t be more thrilled to be a part of this effort, one that will be key to our positioning in other industries that want to realize the benefits of digitization.”

Toru Nishikawa, CEO, Preferred Networks
“PFN is excited that this collaboration will further accelerate the advancement of the manufacturing industry. Since the start of our work with FANUC, leveraging machine learning and artificial intelligence has been aimed not only at making machines and robots smarter, but also towards a continuous improvement of manufacturing productivity through intelligent real-time coordination and collaboration between robots and machines. We are confident that FIELD will play a central role in making that vision a reality.

  

About FANUC CORPORATION
FANUC CORPORATION, headquartered at the foot of Mt. Fuji, Japan, is the global leader and the most innovative manufacturer of Factory Automation, Robots and ROBOMACHINE’s in the world. With 252 offices in 46 countries, FANUC provides world-class service and support to customers globally. Since its inception in 1972, FANUC has contributed to the automation of machine tools as a pioneer in the development of computer numerical control equipment. FANUC technology has been a leading force in a worldwide manufacturing revolution, which evolved from the automation of a single machine to the automation of entire production lines. For more information visit: http://www.fanuc.co.jp/eindex.htm.

About Cisco
Cisco (NASDAQ:CSCO) is the worldwide leader in IT that helps companies seize the opportunities of tomorrow by proving that amazing things can happen when you connect the previously unconnected.
For ongoing news, please go to http://thenetwork.cisco.com.

About Preferred Networks
Preferred Networks Inc. (PFN) is a Tokyo-based startup focusing on applications of latest artificial intelligence technologies to emerging problems in the Internet of Things (IoT). PFN’s vision is the realization of Deep Intelligence – a future IoT in which all devices, as well as the network itself, are intelligent. PFN develops software related to deep learning and IoT, and provides Deep Intelligence in-Motion solutions. PFN collaborates with many world-leading companies in industries, such as FANUC for intelligent robots and Toyota motors for autonomous driving. For more information please visit: www.preferred-networks.jp/en

TOYOTA and PFN announce capital alliance

June 10 Announcement for R&D alliance with FANUC Corporation

FANUC Corporation. and Preferred Networks Inc. (PFN) came to the basic agreement on their alliance in the research and development utilizing machine learning and deep learning technologies, which will enable machine tools and robotics to be highly intelligent.

Background:
IoT has attracted a lot of attention as a key technology to support next generation manufacturing technologies such as Industry 4.0 or Industrial Internet. With the rapidly increasing amount of data, question on how to utilize big data, or how to process the data in real time, remain unsolved. To resolve this question, we focus on machine learning and deep learning technologies which intelligently process big data at edge, at real time and enable high level of automation at manufacturing sites such as machine tools and robotics .

Contents:
Application of machine learning and deep learning has been limited to cyber space. It has not been applied to machine tools and robotics in physical manufacturing sites. We will combine PFN’s expertise in machine learning and deep learning, and FANUC’s numerous technologies in the machines and robotics. We aim best in class automation processes in many layers of manufacturing sites including those where Industry 4.0 applies. The alliance with PFN will cover FANUC’s operations as a whole. Here are some examples that we are aiming for;
Machine Tools and Robotics perform the followings
– Self learning
– Learn to cooperate by themselves
– Self detection of deficiencies and supplement each other
Therefore, it will bring the following results.
– Highly optimized operations of machines and robotics
– Advanced level of protective maintenance
– Non stop factory operations

June 10 Announcement for R&D alliance with Panasonic Corporation

Nishikawa, CEO of PFN Inc. announced in his keynote speech at Interop 2015 Tokyo that PFN will enter into research and development alliance with Panasonic Corporation.

In this alliance, we will aim to integrate Panasonic’s forefront hardware technologies with our machine learning and deep learning technologies which we believe most advanced. Panasonic expects the research outcomes to be applied to automotive area and will also improve functionality of its digital AV devices.

PFN will expand the application of technology elements which we will obtain through the alliance, mainly in deep learning area, into other industry segments.