@inproceedings{bc2ed696daff4db281cbaf3e32f03143,
title = "FPCIM: A Fully-Parallel Robust ReRAM CIM Processor for Edge AI Devices",
abstract = "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.",
author = "Guo, {Yan Cheng} and Lin, {Wei Tien} and Hou, {Tuo Hung} and Chang, {Tian Sheuan}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 ; Conference date: 21-05-2023 Through 25-05-2023",
year = "2023",
doi = "10.1109/ISCAS46773.2023.10181402",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings",
address = "美國",
}
