[Computational Foundation]Year Started : 2018

Affiliation and job title should automatically appear from the information that a researcher registered with researchmap.
Data may be outdated or undocumented.
When there is not a connection via the internet, data are not displayed.

Masaaki Kondo

Development of a Highly Efficient Graph Processing Framework for Edge Computing

Research Director
Katsuki Fujisawa
Tohru Ishihara

An efficient graph processing engine at edge devices is a key to realize a concept of Society 5.0 where various information obtained in the physical world is processed and optimized in a cyber world, and then feedback to the physical world is provided in realtime. In this research, we develop a highly efficient edge graph processing framework including a low-power and low-latency accelerator architecture, system LSI design, and its software environment through co-design with real graph applications. We also consider cooperation between graph processing and AI/annealing computation in the framework.

Hideyuki Suzuki

Computing Technology Based on Spatiotemporal Dynamics of Photonic Neural Networks

Research Director
Hideyuki Suzuki
Jun Tanida

This project aims to establish a new generation of computing technology with photonic neural networks by combining advanced neural and photonic computing technologies. From the viewpoint of spatiotemporal dynamics, this project develops recurrent neural network models for photonic implementation, and propose new computing principles and hardware implementation of photonic neural networks.

Masato Motomura

Steering Toward Spatio-Temporal Computing Architecture Driven by Learning/Math-Scientific Models

Research Director
Hiroki Arimura
Sinichi Minato
Akira Sakai

Through inter-disciplinary collaboration among architecture, machine learning, discrete algorithm, and mathematical science, this project targets creating a new science-technology paradigm for realizing highly secure, dependable, and energy efficient “intelligent computing” that sustains Society 5.0 and beyond. Identifying energy minimization behind machine learning and combinatory optimization problems as a guiding principle, also spatio-temporal computing as an optimal architecture for drastically changing computation workloads, the project will establish a multi-purpose, self-learnable, spatio-temporal energy minimization HW/SW platform. Mathematical science and computer science will together ground break a new research domain.

Quick Access


  • ACT-I
  • ACT-X
  • ALCA
  • manual
  • AIP Network Lab
  • JST ProjectDB
  • Global Activities
  • Diversity
  • SDGs
  • OSpolicy
  • Yuugu
  • Questions