Atsuya Okazaki

Publications

• Phase Change Memory-based Hardware Accelerators for Deep Neural Networks

  • • Geoffrey Burr
    • Pritish Narayanan
    • et al.
  • 2023
  • VLSI Technology 2023

  Invited talk

• Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks

  • • Sidney Tsai
    • Pritish Narayanan
    • et al.
  • 2023
  • ISCAS 2023

  Invited talk

• Analog In-Memory Computing for Deep Neural Network Acceleration

  • • Andrea Fasoli
    • Geoffrey Burr
    • et al.
  • 2023
  • MRS Spring Meeting 2023

  Talk

• Analog-AI: Hardware Acceleration for Deep Neural Network Inference

  • • Geoffrey Burr
    • Sidney Tsai
    • et al.
  • 2023
  • NeuMatDeCaS 2023

  Invited talk

• Impact of PCM noise on the Spiking Restricted Boltzmann Machine via On-Chip Trainable PCM synapses

  • • Uicheol Shin
    • Masatoshi Ishii
    • et al.
  • 2022
  • MRS Fall Meeting 2022

  Talk

• Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers

  • • Atsuya Okazaki
    • Pritish Narayanan
    • et al.
  • 2022
  • ISCAS 2022

  Conference paper

• Fully On-Chip MAC at 14 nm Enabled by Accurate Row-Wise Programming of PCM-Based Weights and Parallel Vector-Transport in Duration-Format

  • • Pritish Narayanan
    • Stefano Ambrogio
    • et al.
  • 2021
  • IEEE T-ED

  Paper

• Fully on-chip MAC at 14nm enabled by accurate row-wise programming of PCM-based weights and parallel vector-transport in duration-format

  • • P. Narayanan
    • S. Ambrogio
    • et al.
  • 2021
  • VLSI 2021

  Conference paper

• On-Chip Trainable 1.4M 6T2R PCM Synaptic Array with 1.6K Stochastic LIF Neurons for Spiking RBM

  • • Masatoshi Ishii
    • U. Shin
    • et al.
  • 2019
  • IEDM 2019

  Conference paper

• Analysis of Effect of Weight Variation on SNN Chip with PCM-Refresh Method

  • • A. Nomura
    • M. Ito
    • et al.
  • 2019
  • Neural Processing Letters

  Paper