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		题名	作者	年代	出处	被引量
	1	NAND-SPIN-based processing-in-MRAM architecture for convolutional neural network acceleration显示文摘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 study,we propose a NAND-SPIN-based PIM architecture for efficient convolutional neural network(CNN)acceleration.A straightforward data mapping scheme is exploited to improve 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~2.6×speedup and~1.4×improvement in energy efficiency over state-of-the-art PIM solutions.	Yinglin ZHAO Jianlei YANG Bing LI Xingzhou CHENG Xucheng YE Xueyan WANG Xiaotao JIA Zhaohao WANG Youguang ZHANG Weisheng ZHAO	2023	Science China(Information Sciences)2023，66，4：	0

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