节能MAC操作的全数字全精度sram计算与约简树

Dengfeng Wanq, Zhi Li, C. Chang, Weifeng He, Yanan Sun
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摘要

本文提出了一种基于全数字全精度静态随机存取存储器的内存中计算(SRAM-CIM)宏,该宏具有基于压缩器的约简树(CRT),用于节能的乘法和累积(MAC)操作。该CRT由28T/18T/14T 3-2混合式压缩器(全加法器,FAs)和18T半加法器(HAs)组成,与传统的二进制加法器树(BAT)相比,功耗更低,电源电压更低。实验结果表明,与BAT相比,所提出的CRT的功率和面积分别降低了56.15%和28.11%。与之前采用BAT的全数字全精度SRAM-CIM宏相比,采用CRT的SRAM-CIM宏的单位面积能效提高了78.07%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
All-Digital Full-Precision In-SRAM Computing with Reduction Tree for Energy-Efficient MAC Operations
This paper proposes an all-digital full-precision static random-access memory based computing-in-memory (SRAM-CIM) macro with compressor-based reduction tree (CRT) for energy-efficient multiplication-and-accumulation (MAC) operations. The proposed CRT composed of hybrid 28T/18T/14T 3–2 compressors (full adders, FAs) and 18T half adders (HAs) with lower supply voltage consumes lower power compared to conventional binary adder tree (BAT). The experimental results show that the power and area of the proposed CRT are reduced by up to 56.15% and 28.11%, respectively, as compared to BAT. The proposed SRAM-CIM macro with CRT achieves 78.07% higher energy efficiency per unit area, compared to previous all-digital full-precision SRAM-CIM macro with BAT.
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