2022 International Conference on Solid State Devices and Materials

講演情報

Oral Presentation

02: Advanced and Emerging Memories / New Applications

[F-7] In-Memory and Unconventional Computing

2022年9月29日(木) 09:00 〜 10:15 201 (2F)

Session Chair: Halid Mulaosmanovic (GlobalFoundries), Hiroki Sasaki (MIRISE Technologies Corp.)

09:30 〜 09:45

[F-7-02] Tiny and Error Torrent Convolutional LSTM for Event-based Vision Sensor with ReRAM Computation-in-Memory

〇Tomoki Kobayashi1, Kazuhide Higuchi1, Naoko Misawa1, Chihiro Matsui1, Ken Takeuchi1 (1. Univ. of Tokyo (Japan))

Presentation style: Online

https://doi.org/10.7567/SSDM.2022.F-7-02

This paper proposes tiny and error tolerant Convolu-tional LSTM (ConvLSTM) for event-based vision sensor (EVS) data processing with ReRAM Computa-tion-in-Memory (CiM). To realize tiny ConvLSTM CiM, reduction of the bit precision of each ConvLSTM layer and weight is considered. On the other hand, reducing the bit precision degrades inference accuracy and error tolerance. ReRAM has low switching current and high scalability, but ReRAM has non-ideality such as conductance variation due to program/read disturb. Thus, the bit precision of the weight is optimized to accept bit error rate of 0.1%. Therefore, the proposed tiny ConvLSTM can reduce the size of memory by 91% compared with conventional ConvLSTM.

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