FRM-CIM: Full-Digital Recursive MAC Computing in Memory System Based on MRAM for Neural Network Applications
Authors: 
Jinkai Wang, Zekun Wang, Bojun Zhang, Zhengkun Gu, Youxiang Chen, 
Weisheng Zhao, Yue ZhangAuthors Info & Claims
Jinkai Wang, Zekun Wang, Bojun Zhang, Zhengkun Gu, Youxiang Chen, Weisheng Zhao, Yue ZhangAuthors Info & ClaimsArticle No.: 186, Pages 1 - 6
Abstract
Computing in memory (CIM) realizes energy-efficient neural network algorithms by implementing highly parallel multiply-and-accumulate (MAC) operation. However, the MAC delay of CIM will sharply increase with the improvement of computing precision, which restricts its development. In this work, we propose a full-digital recursive MAC (FRM) operation based on spin-transfer-torque magnetic random access memory (STT-MRAM) CIM system to enable fast and energy-efficient image recognition application. First, the fast FRM scheme is proposed by utilizing the recursive operations of read and addition in segmented bit-line array, which effectively reduces the delay of MAC operations to 3.5ns and 4ns for 8-bit and 16-bit input and weight precision, respectively. Second, we design an image recognition system using FRM-CIM architecture as the processing element (PE), where the adaptive pruning method for layers is proposed to improve the compatibility of it with the neural network. By performing image recognition for the MNIST and CIFAR-10 datasets, results show that the throughput and energy efficiency of the FRM-CIM system are 58.51TOPS/mm2 and 11.3--56.72 TOPS/W under 8--16-bit precision, which are improved by 4.3 times and 2.6 times compared with the state-of-the-art works. Finally, the recognition accuracy can reach 96.65% and 82.7% on MNIST and CIFAR-10, respectively.
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- FRM-CIM: Full-Digital Recursive MAC Computing in Memory System Based on MRAM for Neural Network Applications
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Published: 07 November 2024
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