Computing Architecture

Research Staff

  • Prof.Yasuhiko Nakashima

    Yasuhiko Nakashima

  • Prof. Mutsumi Kimura

    Affiliate Prof.
    Mutsumi Kimura

  • Assoc.Prof. ZHANG, Renyuan

    Renyuan Zhang

  • Assist.Prof. Tran Thi Hong

    Tran Thi Hong

E-mail { nakashim, , rzhang, hong }[at]

Research Areas

Power efficient near-data memory array accelerators for the Post-Moore Generation

Research and development of highly efficient computing systems, accelerators and LSIs for image processing and big data processing, such as graph processing and machine learning

EMAXV,VR: A large-scale CGRA for image recognition

IMAX: A small footprint near-memory accelerator for AI

Compact and efficient approximate computing VLSIs for the Post-Moore Generation

Research and development of reconfigurable approximate computing VLSI architectures with compact circuits, low energy, and function-flexibility for multi-operand computations, which can be efficiently employed in parallel computing tasks

Various non-binary-based computing methodologies such as neuromorphic and stochastic computing for the Post-Moore generation

Exploring analog-digital-hybrid CGRA platforms

Neuromorphic LSIs for the Post-Moore Generation

Research and development of super compact and low power neuromorphic integrated systems for artificial intelligence

Amorpous metal-oxide semiconductor thin-film synapses for 3D structures

Neuromorphic architecture and leaning rules for astronomical scale integration

Brain-type integrated systems with artificial humanity

Energy efficient system architecture for next generation machine learning

Research and development of next generation machine learning system architecture with edge computing

Probabilistic reasoning algorithms with Bayesian networks

IoT + blockchain for secure smart systems

IoT and blockchain technologies are combined to develop smart systems such as smart healthcare systems, smart city management, etc. Secure health monitoring systems for hospitals are currently being researched.

Key Features

In our laboratory, we study state-of-the-art technologies for next-generation computing paradigms. Our goal is to realize environment-friendly, high-performance, and robust computer systems under energy constraints. From a wide viewpoint (from new theories to LSI implementations), we promote cutting-edge research and the highest degree of education within various research themes, particularly: high-performance, low-energy and dependable computation, and hardware/software co-design.

Fig.1: Power Efficient Near-Data Memory Array Accelerators and FPGA Systems

Fig.1: Power Efficient Near-Data Memory Array Accelerators and FPGA Systems

Fig.2:Analog Acelerators

Fig.2: Analog Acelerators

Fig.3:Analog Neural Network LSIs

Fig.3: Analog Neural Network LSIs

Fig.4:Blockchain for IoT

Fig.4: Blockchain for IoT