增强AI加速:具有激活函数的免校准,pvt鲁棒模拟内存中计算宏

IF 2.2 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Hechen Wang;Renzhi Liu;Richard Dorrance;Deepak Dasalukunte;Niranjan Mylarappa Gowda;Brent Carlton
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引用次数: 0

摘要

大多数模拟内存计算(ACiM)只关注多重累积(MAC)操作,而忽略了数字域的激活函数(AF)。频繁的数据转换大大降低了模拟计算的效益。这封信提出了一个高效的8位内存MAC与混合电容梯子。然后,引入了稀疏感知的R-2R DAC和重用MAC中的电容阶梯的嵌入式SAR-ADC,以减少转换开销。包括两种片上AF方案,以进一步提高效率。最后,差分信号路径提供了一阶PVT抵消,提高了计算精度,减少了校准的需要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing AI Acceleration: A Calibration-Free, PVT-Robust Analog Compute-in-Memory Macro With Activation Functions
Most analog compute-in-memory (ACiM) works only focus on the multiple–accumulate (MAC) operation while neglecting the activation function (AF) in the digital domain. The frequent data conversion greatly reduces the benefits obtained by analog computing. This letter proposes an efficient 8-bit in-memory MAC with hybrid capacitor ladders. Then, a sparsity-aware R-2R DAC and an embedded SAR-ADC that reuses the capacitor ladders in the MAC are introduced to reduce the conversion overhead. Two on-chip AF schemes are included to further improve efficiency. Finally, differential signal path offers first-order PVT cancellation that improves computing accuracy and reduces the need for calibration.
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来源期刊
IEEE Solid-State Circuits Letters
IEEE Solid-State Circuits Letters Engineering-Electrical and Electronic Engineering
CiteScore
4.30
自引率
3.70%
发文量
52
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