基于6T SRAM阵列的区域高效和容差内存BNN计算

Jinseok Kim, Jongeun Koo, Taesu Kim, Yulhwa Kim, Hyungjun Kim, Seunghyun Yoo, Jae-Joon Kim
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引用次数: 41

摘要

我们首次介绍了一种基于SRAM的二进制神经网络(BNN)硬件,该硬件使用单个6T SRAM单元进行XNOR操作。对于XNOR操作,该单元比之前的8T位单元小45%。我们还提出了一个内存校准和批归一化,以实现更可靠的操作,在存在工艺变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Area-Efficient and Variation-Tolerant In-Memory BNN Computing using 6T SRAM Array
We introduce a SRAM-based binary neural network (BNN) hardware which uses a single 6T SRAM cell for XNOR operation for the first time. The cell is 45% smaller than the previous 8T bitcell for XNOR operation. We also propose an in-memory calibration and batch normalization to achieve more reliable operation under the presence of process variation.
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