期刊
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
卷 27, 期 4, 页码 988-991出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVLSI.2018.2882194
关键词
3-D NAND flash; neural network; vector-matrix multiplication (VMM); weighted sum
Three-Dimensional NAND flash technology is one of the most competitive integrated solutions for the high-volume massive data storage. So far, there are few investigations on how to use 3-D NAND flash for in-memory computing in the neural network accelerator. In this brief, we propose using the 3-D vertical channel NAND array architecture to implement the vector-matrix multiplication (VMM) with for the first time. Based on the array-level SPICE simulation, the bias condition including the selector layer and the unselected layers is optimized to achieve high computation accuracy of VMM. Since the VMM can be performed layer by layer in a 3-D NAND array, the read-out latency is largely improved compared to the conventional single-cell read-out operation. The impact of device-to-device variation on the computation accuracy is also analyzed.
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