FPCIM: A Fully-Parallel Robust ReRAM CIM Processor for Edge AI Devices

Yan Cheng Guo, Wei Tien Lin, Tuo Hung Hou, Tian Sheuan Chang

研究成果: Conference contribution同行評審

摘要

Computing-in-memory (CIM) is popular for deep learning due to its high energy efficiency owing to massive parallelism and low data movement. However, current ReRAM based CIM designs only use partial parallelism since fully parallel CIM could suffer lower model accuracy due to severe nonideal effects. This paper proposes a robust fully-parallel ReRAM-based CIM processor for deep learning. The proposed design exploits the fully-parallel computation of a 1024x1024 array to achieve 110.59 TOPS and reduces nonideal effects with in-ReRAM computing (IRC) training and hybrid digital/IRC design to minimize the accuracy loss with only 1.55%. This design is programmable with a compact CIM-oriented instruction set to support various 2-D convolution neural networks (NN) as well as hybrid digital/IRC designs. The final implementation achieves a 2740.41 TOPS/W energy efficiency at 125MHz with TSMC 40nm technology, which is superior to previous designs.

原文English
主出版物標題ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665451093
DOIs
出版狀態Published - 2023
事件56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States
持續時間: 21 5月 202325 5月 2023

出版系列

名字Proceedings - IEEE International Symposium on Circuits and Systems
2023-May
ISSN(列印)0271-4310

Conference

Conference56th IEEE International Symposium on Circuits and Systems, ISCAS 2023
國家/地區United States
城市Monterey
期間21/05/2325/05/23

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