Paper accepted: Embedded Systems Week (ESWEEK) CASES

The following paper has been accepted for the International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES) 2022 during Embedded Systems Week (ESWEEK), scheduled for October 7-14, 2022. (The paper will be presented at ESWEEK and published in TCAD).

Sosei Ikeda, Hiromitsu Awano, and Takashi Sato, “Hardware-friendly delayed feedback reservoir for multivariate time series classification,” IEEE Transactions on Computer-Aided Design of integrated circuits and systems (TCAD), to appear.

SSDM: Paper accepted

The following paper has been accepted for presentation at the International Conference on Solid State Devices and Materials (SSDM) 2022. This work is the result of joint research with National Institute of Advanced Industrial Science and Technology (AIST).

Yuto Kaneiwa, Kazunori Kuribara, and Takashi Sato, “Aging-robust Amplifier Design Using Low Voltage Organic Semiconductor Loads,” in Proc. International Conference on Solid State Devices and Materials (SSDM), September 2022 (to appear).

The paper below is also accepted.

M. Hashimoto, Y. Zhang, and K. Ito, “Neutron-Induced Stuck Error Bits and Their Recovery in DRAMs on GPU Cards,” Proceedings of International Conference on Solid State Devices and Materials (SSDM), to appear.

DAC 2022

Prof. Sato and M2 Matsuoka gave a tutorial talk at the Design Automation Conference (DAC) held in San Francisco, CA, USA, July 10-14, 2022, on “FHE Circuit Construction and Synthesis” at Design Automation Conference (DAC). (Tutorial was July 11)

DAC59 entrance DAC59 registration DAC59 tutorial DAC59 Oracle park

Paper accepted: IEEE Transactions on Computers

The following paper has been accepted for publication in IEEE Transactions on Computers (TC). This is an international collaborative paper with the University of Notre Dame, IBM, Beihan University, Fudan University, and other universities in the United States.

  • Tianchen Wang, Jiawei Zhang, Jinjun Xiong, Song Bian, Zheyu Yan, Meiping Huang, Jian Zhuang, Takashi Sato, Yiyu Shi, and Xiaowei Xu, “VisualNet: An end-to-end human visual system inspired framework to reduce inference latency of deep neural networks,” IEEE Transactions on Computers (accepted for publication)
    DOI: 10.1109/TC.2022.3188211