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MARSv2: Multicore and Programmable Reconstruction Architecture SRAM CIM-Based Accelerator with Lightweight Network
Conference paper

MARSv2: Multicore and Programmable Reconstruction Architecture SRAM CIM-Based Accelerator with Lightweight Network

Chia-Yu Hsieh, Shih-Ting Lin, Zhaofang Li, Chih-Cheng Lu, Meng-Fan Chang and Kea-Tiong Tang
Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022, pp.383-386
2022
DOI: https://doi.org/10.1109/AICAS54282.2022.9870005

Abstract

CNN accelerator computing-in-memory deep learning sparsity Artificial Intelligence Computer Science Applications Computer Vision and Pattern Recognition Hardware and Architecture Human-Computer Interaction Electrical and Electronic Engineering
Computing-in-memory (CIM) systems reduce the degree of large-scale data movement by performing computation on the memory; this avoids a von Neumann bottleneck. Because of its low-power characteristic, CIM has demonstrated great potential for increasing the energy efficiency of edge devices. This paper presents a multicore and programmable reconstruction architecture using static random-access memory (SRAM) CIM-based accelerator with lightweight network. The proposed architecture uses SRAM CIM macro as the processing element, supporting sparse convolutional neural network computing. This architecture achieves 15.16 TOPS/W system energy efficiency and 747.6 GOPS on the CIFAR10 data set.

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