Press release

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.