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arXiv · 2204.09989

NAND-SPIN-Based Processing-in-MRAM Architecture for Convolutional Neural Network Acceleration

Yinglin Zhao·Jianlei Yang·Bing Li·Xingzhou Cheng·Xucheng Ye·Xueyan Wang·Xiaotao Jia·Zhaohao Wang·Youguang Zhang·Weisheng Zhao
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Abstract

The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing "power wall" and "memory wall" problems. To resolve those problems, processing-in-memory (PIM) architectures are developed to bring computation logic in or near memory to alleviate the bandwidth limitations during data transmission. NAND-like spintronics memory (NAND-SPIN) is one kind of promising magnetoresistive random-access memory (MRAM) with low write energy and high integration density, and it can be employed to perform efficient in-memory computation operations. In this work, we propose a NAND-SPIN-based PIM architecture for efficient convolutional neural network (CNN) acceleration. A straightforward data mapping scheme is exploited to improve the parallelism while reducing data movements. Benefiting from the excellent characteristics of NAND-SPIN and in-memory processing architecture, experimental results show that the proposed approach can achieve $\sim$2.6$\times$ speedup and $\sim$1.4$\times$ improvement in energy efficiency over state-of-the-art PIM solutions.

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Yinglin Zhao, Jianlei Yang, Bing Li, Xingzhou Cheng, Xucheng Ye, Xueyan Wang, Xiaotao Jia, Zhaohao Wang, Youguang Zhang, Weisheng Zhao. 2022-04-21. NAND-SPIN-Based Processing-in-MRAM Architecture for Convolutional Neural Network Acceleration. https://doi.org/10.1007/s11432-021-3472-9

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