Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu’s output. The Japanese multinational has made a raft of HPC and AI-related announcements over the last few weeks. One of the most interesting developments is the advance of a custom AI processor, the Deep Learning Unit (DLU). With only a brief appearance in a 2016 press release, a fuller picture emerged during the International Supercomputing Conference in June.
As revealed in a presentation from Fujitsu’s Takumi Maruyama (senior director, AI Platform business unit), the processor features mixed-precision optimizations (8-bit, 16-bit and 32-bit) and a low power consumption design, with a stated goal of achieving a 10x performance/per watt advantage compared to competitors. The target energy efficiency gain relies on Fujitsu’s “deep learning integer,” which the company says reaches effective precision (on par with 32-bit) using 8- and 16-bit data sizes. The approach is reminiscent of that used by Intel’s Knights Mill processor (see coverage here) with Intel claiming INT32 accuracy with INT16 inputs (using INT32 accumulated output).
The massively parallel chip employs a few large master cores connected to many Deep Learning Processing Units (DPUs). One DPU consists of 16 DPEs (Deep learning processing elements). The DPE includes a large register file and wide SIMD execution units. Linked with Fujitsu’s Tofu interconnect technology, the design is scalable for very large neural networks.
Fujitsu’s roadmap for the DLU includes multiple generations over time: a first-gen coprocessor is set to debut in 2018, followed by a second-gen embedded host CPU. More forward-looking are potential specialized processors targeting neuromorphic or combinatorial optimization applications.
Also at ISC, Fujitsu announced it’s building a nearly 3.5 petaflops (peak) system for Taiwan’s National Center for High-performance Computing, National Applied Research Laboratories (NCHC). The supercomputer is expected to come online in May 2018, at which time it will become the fastest computer in the country.
“The new system will serve as the core platform for research and development in Taiwan, fostering the development and growth of Taiwan’s overall industries and economy,” said Fujitsu in an official statement. In addition to accelerating current research, there will be a focus on accommodating new research fields, such as AI and big data.
The 715 node warm water-cooled cluster will be equipped with Skylake processors and connected with Intel Omni-Path technology. Nvidia P100 GPUs will be installed on 64 nodes, providing over a third (1.35 petaflops) of total theoretical peak performance (3.48 petaflops).
The Information Technology at Kyushu University in Japan has also placed an order for a Fujitsu system, a 10-petaflopper (peak) that is scheduled for deployment in October.
“This system will consist of over 2,000 servers, including the Fujitsu Server PRIMERGY CX400, the next-generation model of Fujitsu’s x86 server….This will also be Japan’s first supercomputer system featuring a large-scale private cloud environment constructed on a front-end sub system, linked with a computational server of a back-end sub system through a high-speed file system,” according to the release.
The new supercomputer will be integrated with three existing HPC systems at the Research Institute for Information Technology. The goal is to create an environment that “extend[s] beyond the current large-scale computation and scientific simulations, to include usage and research that require extremely large-scale computation, such as AI, big data, and data science.”
New AI-Based Algorithm Monitors Heat Stress
As temperatures rise, the health of employees in active outdoor roles, for example security guards or delivery professionals, is threatened. In Japan, 400-500 workplace casualties are attributable to heat stroke each year, leading companies to take measures to safeguard employees working in extreme conditions.
Fujitsu has developed an algorithm to bolster summer safety in the workplace. Based on Fujitsu’s Human Centric AI platform, Zinrai, the algorithm estimates on-going heat stress in workers. Fujitsu will release the algorithm as part of its digital business platform, MetaArc, which uses IoT to support on-site safety management. It is also conducting an internal trial from June to September at its Kawasaki Plant.
Says the company, “Sites where security and other duties typically take place may be locations where workers are susceptible to heat stress. However, changes in physical condition vary according to the individual, making it difficult to take uniform measures. This newly developed algorithm makes it possible to estimate the accumulation of heat stress on a per person basis, to tailor ways to protect people based on individual conditions.”
Machine Learning Advances Lung Disease Diagnosis
Fujitsu Laboratories Ltd. in partnership with Fujitsu R&D Center Co., Ltd., has developed a technology to improve the diagnosis for a group of lung diseases that includes pneumonia and emphysema. The technology retrieves similar disease cases from a computed tomography (CT) database based on abnormal shadows implicated in these disease states. The technology is especially needed for diffuse lung diseases like pneumonia, where the abnormal shadows are spread throughout the organ in all directions. These three-dimensional problems require a great deal of knowledge and experience on the clinician’s part to interpret and diagnose.
As explained by Fujitsu “the technology automatically separates the complex interior of the organ into areas through image analysis, and uses machine learning to recognize abnormal shadow candidates in each area. By dividing up the organ spatially into periphery, core, top, bottom, left and right, and focusing on the spread of the abnormal shadows in each area, it becomes possible to view things in the same way doctors do when determining similarities for diagnosis.”
Early studies using real-world data indicate a high-accuracy for the approach, which has the potential to save lives by reducing the time it takes to achieve a correct diagnosis.
Promoting open data usage in the Japanese Government
On June 28, Fujitsu announced that it will be part of a project run by the Cabinet Secretariat’s National Strategy Office of Information and Communications Technology to promote the use of open data held by the national or regional public organizations. The goal is to make open data, such as population statistics, industry compositions, and geographic data, more accessible and by doing so strengthen national competitiveness.
Fujitsu will leverage its Zinrai platform to develop a test system that can laterally search for data across multiple government systems, relating texts that have the same meaning. The system will also “learn” from users’ search results such that it can fine-tune its suggestions.
The study, “Creating an AI-Based Multi-Database Search and Best-Response Suggestion System (Research Study on Increasing Usability of Data Catalog Sites),” will run through until December 22, 2017. Fujitsu expects the trial to result in a proposal to the Strategy Office of Information and Communications Technology for implementation.
The Zinrai AI framework: