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Fujitsu’s Novel AI Deep Learning Technology Sheds New Light on Real-World Problems

Fujitsu EMEIA

News facts:
  • Fujitsu Laboratories of Europe has developed a cost-effective, memory efficient, and scalable deep learning technology for AI applications, overcoming the current GPU memory restrictions
  • Part of Fujitsu’s human-centric Artificial Intelligence (AI) Zinrai technology initiative, the solution enables existing computing infrastructures to address largescale AI challenges, without the need for additional major investment in infrastructure
  • It deploys a novel model parallelism mechanism to improve and automate Deep Neural Network memory distribution, achieving 90 percent efficiency in initial trials
  • It enables the use of higher resolution images to recognize tiny elements, improving classification into finer categories, and enabling advances that impact our daily lives
  • The technology will be demonstrated for the first time at the 2017 Fujitsu Innovation Gathering, taking place during the German stop of the Fujitsu World Tour 2017 on May 23 in Berlin, Germany
Berlin, May 23, 2017 – Fujitsu today announces an important deep learning technology breakthrough, developing a novel and highly efficient memory distribution mechanism for Deep Neural Networks (DNNs). Used extensively for many AI applications involving speech and object recognition and classification, the use of advanced DNNs requires massive computational resources, imposing severe demands on existing computing infrastructures. With Fujitsu Laboratories of Europe’s new deep learning solution, model-parallelism is used to distribute the DNN memory requirements in an automated, transparent and easily managed way. As a result, the capacity of existing infrastructures to address large-scale AI applications is considerably enhanced without the need for further investment.
Dr Tsuneo Nakata, CEO at Fujitsu Laboratories of Europe, explains the benefits of this new deep learning technology:
“In recent years, we have seen a massive surge in technological advancements that use hardware accelerators to support the enormous scale of computations required to build Deep Neural Networks (DNN) for AI applications. The continuous increase in the computational costs of DNNs is a major challenge, particularly when the model size of DNNs increases to the point that it cannot fit into the memory of a single accelerator. Wider and deeper Neural Networks are needed, together with finer classification of categories, to address emerging AI challenges. Our solution addresses this directly, distributing DNN memory requirements onto multiple machines. With our technology, it is possible to expand the size of neural networks that can be learned on multiple machines, enabling the development of more accurate and large-scale DNN models.“
The new solution achieves this new memory distribution process by transforming the layers of arbitrarily designed neural networks into equivalent networks in which some, or all, of its layers are replaced by a number of smaller sub-layer parts. These sub-layer parts are designed to be functionally equivalent to the original layers, but are computationally much more efficient to execute. Importantly, since the original and new layers stem from the same profile, the training process of the now transformed and distributed DNN converges to that of the original DNN at no added cost.
Fujitsu Laboratories of Europe evaluated the new technology extensively, including applying the new mechanism to Caffe, an open source deep learning framework widely used by R&D communities around the world. The solution achieved more than 90 percent efficiencies in memory distribution when transforming the fully connected layers of AlexNet onto several NVIDIA GPUs. As a hardware independent technology, it has the capabillity of exploiting the computational power of both conventional processing units as well as current and emerging hardware accelerators including, for example, NVIDIA GPUs, Intel Xeon Phi, FPGAs, ASICs etc. or any other alternative hardware chips specifically tailored for increasing the computational efficiencies in Deep Learning.

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Figure : Using model-parallelism the DNN memory relink added tquirements are reduced and distributed in an automated, transparent and easily managed approach.

Example applications for the new solution include healthcare analysis (e.g. diabetic retinopathy detection); satellite image classification and analysis; natural language processing, where largescale deep learning models are required to model and learn the full complexity of the human language; largescale graph-based data involving IoT devices, financial transactions, social network services etc.
Fujitsu Laboratories of Europe is a centre of excellence for Fujitsu’s advanced research into machine learning and deep learning, as part of the digital solutions and services being developed under Fujitsu's human-centric AI initiative called Zinrai. Its activities include extensive collaboration and co-creation with Fujitsu customers and research organisations across EMEIA, including San Carlos Clinical Hospital in Madrid (with the HIKARI AI healthcare solution), the University of Seville (data analytics for tourism applications), and the 5G Innovation Centre in the UK.

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About Fujitsu

Fujitsu is the leading Japanese information and communication technology (ICT) company, offering a full range of technology products, solutions, and services. Approximately 155,000 Fujitsu people support customers in more than 100 countries. We use our experience and the power of ICT to shape the future of society with our customers. Fujitsu Limited (TSE: 6702) reported consolidated revenues of 4.5 trillion yen (US $40 billion) for the fiscal year ended March 31, 2017. For more information, please see http://www.fujitsu.com.

About Fujitsu Laboratories of Europe

Established in 2001 and with an active presence in Europe since 1990, Fujitsu Laboratories of Europe Limited represents Fujitsu Laboratories across EMEIA, focusing on regional initiatives that reflect the diverse mix of countries and ideologies. Fujitsu Laboratories of Europe is focused on the creation of cutting-edge solutions that benefit society, adopting a co-creation strategy and working with customers, collaboration partners and society as a whole to pioneer a new generation of user-centric applications and services underpinned by creative information analytics. It works on the principle of open innovation, with particular emphasis on Future Networking & Wireless Standards, Artificial Intelligence, Advanced Data Analytics and Deep Learning, Social Innovations and Supercomputer Applications. This also involves adopting a co-creation strategy, working together to pioneer a new generation of user-centric applications and services underpinned by creative information analytics. For more information, please see http://www.fujitsu.com/uk/fle/.

Media contacts

Georgina Garrett
Director
Garrett Axford Ltd (on behalf of Fujitsu Laboratories of Europe Ltd)

Phone: Phone: +44 1903 854900
E-mail: E-mail: mail@garrett-axford.co.uk


All other company or product names mentioned herein are trademarks or registered trademarks of their respective owners. Information provided in this press release is accurate at time of publication and is subject to change without advance notice.

Date: 23 May, 2017
City: Berlin