一种改进基4输入规范带符号数字权的低功耗内存乘法andAccumulation阵列
A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights
摘要：
处理单元和存储单元之间的大量数据传输一直是现代冯-纽曼计算系统的主要瓶颈，特别是在用于人工智能（AI）任务时。内存计算（CIM）在降低延迟和功耗方面显示出巨大的潜力。然而，传统的模拟CIM方案遭受可靠性问题，这可能会显着退化的计算精度。最近，已经提出了具有数字化输入数据和权重的CIM方案以用于高可靠性计算。然而，数字存储器和输入数据的特性没有被充分利用。本文提出了一种新的低功耗CIM方案，通过在输入端使用修改的基4（M-RD 4）布斯算法和修改的规范有符号数字（M-CSD）的网络权重，以进一步降低功耗。仿真结果表明，M-Rd 4和M-CSD在LeNet和AlexNet上分别使$1\times1$的比例降低了78.5%和80.2%，计算效率平均提高了41.6%.定点8位时的计算功耗为60.68 TOPS/s/W。
英文摘要：
A mass of data transfer between the processing and storage units has been the leading bottleneck in modern Von-Neuman computing systems, especially when used for Artificial Intelligence (AI) tasks. Computing-in-Memory (CIM) has shown great potential to reduce both latency and power consumption. However, the conventional analog CIM schemes are suffering from reliability issues, which may significantly degenerate the accuracy of the computation. Recently, CIM schemes with digitized input data and weights have been proposed for high reliable computing. However, the properties of the digital memory and input data are not fully utilized. This paper presents a novel low power CIM scheme to further reduce the power consumption by using a Modified Radix-4 (M-RD4) booth algorithm at the input and a Modified Canonical Signed Digit (M-CSD) for the network weights. The simulation results show that M-Rd4 and M-CSD reduce the ratio of $1\times1$ by 78.5\% on LeNet and 80.2\% on AlexNet, and improve the computing efficiency by 41.6\% in average. The computing-power rate at the fixed-point 8-bit is 60.68 TOPS/s/W.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
