[Computational Foundation] Year Started : 2019

Hideharu Amano

Development of an integrated multi-node system for multi-access edge computing

Research Director
Hideharu Amano

Professor
Faculty of Science and Technology
Keio University

Collaborator
Masahiro Iida Professor
Faculty of Advanced Science and Technology
Kumamoto University
Midori Sugaya(Shimazaki) Professor
Faculty of Engineering
Shibaura Institute of Technology
Hiroaki Nishi Professor
Faculty of Science and Technology
Keio University
Kazutosi Wakabayashi Senior Fellow
System Design Research Center
The University of Tokyo
Outline

Crust-core HUB is an integrated switch for ensuring the bandwidth and latency which combines a reconfigurable accelerator and a general-purpose processor. Connecting FPGAs and other domain-specific computers with Crust-core HUB, a powerful yet energy-efficient computing system is built for the future multi-access edge computing system. System software and design CAD for such a system are also developed, and the prototype system will be applied to the real smart city.

Isao Inoue

Platform for real-time learning at the edge with spiking neural networks

Research Director
Isao Inoue

Senior Researcher
Research Institute of Advanced Electronics and Photonics
National Institute of Advanced Industrial Science and Technology

Collaborator
Tetsuya Iizuka Associate Professor
Graduate School of Engineering
The University of Tokyo
Kantaro Fujiwara Project Associate Professor
International Research Center for Neurointelligence
The University of Tokyo
Yasushi Hotta Asssoiate Professor
Graduate School of Engineering
University of Hyogo
Takeaki Yajima Associate Professor
Graduate School and Faculty of Information Science and Electrical Engineering
Kyushu University
Outline

Back-propagation (BP) of machine learning consumes immense energy for its enormous logic operations. On the other hand, the so-called “attractor formation”, in which firings of neurons of the spiking neural network (SNN) spontaneously converge to some specific loops, has the same function as BP does in the machine learning. With this intriguing feature, we are sure that we will be able to perform on-site ultra-low-power real-time learning/inference. Thus, in this project, we aim to corroborate this scenario by building the attractor SNN circuits consisting of our uniquely developed neuromorphic devices. The SNN circuits are implemented to a personal-authentication device. Finally, the device should learn and infer the unconscious behaviours of a human without accessing the internet nor consuming considerable energy.

Shigeo Sato

Fundamentals of spintronics-based edge computing hardware

Research Director
Shigeo Sato

Professor
Research Institute of Electrical Communication
Tohoku University

Collaborator
Takahiro Hanyu Professor
Research Institute of Electrical Communication
Tohoku University
Shunsuke Fukami Professor
Research Institute of Electrical Communication
Tohoku University
Outline

Towards the realization of Society 5.0, functionalities such as low-power consumption and real-time processing are required in edge computing systems. We therefore develop spintronic devices having non-volatile analog-storage functions and rich dynamics to meet these requirements and research analog-digital-mixed CMOS integrated circuit technologies to maximally utilize the aforementioned device characteristics. We also investigate the optimal hardware architecture for spintronics-based edge computing and finally identify the best way for the practical implementation of our hardware through experimental verifications.

Nozomu Togawa

Research on Ising Computation for Real-World Geographic Information Systems

Research Director
Nozomu Togawa

Professor
Faculty of Science and Engineering
Waseda University

Collaborator
Toshinori Takayama Expert Engineer
Technical Division
ZENRIN DataCom CO., LTD.
Outline

In this project, we focus on geographic information applications, which are necessary for Society 5.0, and newly propose Ising programming for them. Firstly, we pick up variety of geographic information problems, including cyclic-route search problems as well as load packing problems with many types of constraints and represent them onto Ising models. After that, we embed these Ising models into practical Ising computers and finally solve real-sized geographic information problems with real types of constraints.

Naofumi Honma

Secure Information Processing Technology Based on Post-Quantum Encrypted Computing

Research Director
Naofumi Honma

Professor
Research Institute of Electrical Communication
Tohoku University

Collaborator
Takashi Sato Professor
Graduate School of Informatics
Kyoto University
Masanori Hashimoto Professor
Graduate School of Informatics
Kyoto University
Yuichi Hayashi Professor
Graduate School of Science and Technology
Nara Institute of Science and Technology
Outline

This research project aims at studying the theory of secure information processing technologies that can execute secret computation based on post-quantum homomorphic encryption in a tamper-resistant manner with extremely high speed and high efficiency, and developing a secure secret computation platform that integrates the technologies. In addition, as new applications pioneered by the platform, we will develop those of secret statistical analysis and secret inference (that conceals input/output and learned parameters in machine learning) based on high-speed/high-efficiency secret computation.

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