利用残差放大进行输入调节的 2-10-b 输出精度可重构内存计算巨集

IF 2.2 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Balaji Vijayakumar;Ashwin Balagopal Sundar;Janakiraman Viraraghavan;Varchas Bharadwaj
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引用次数: 0

摘要

人工智能工作负载需要大范围的乘法和累加(MAC)精度。内存计算 (CIM) 引擎中的节距匹配限制将模数转换器 (ADC) 的精度限制在 8 位左右。这封信展示了一种将合适的输入条件 MAC 范围映射到片上 7-b ADC 输入动态范围的方法,从而实现高达 10 位的输出 MAC 精度。在 TSMC 28 纳米工艺中制造了 424 Kb SRAM CIM 宏,每个周期并行计算 72 个 MAC。在标称电源电压下的测量结果显示,2-10 位输出 MAC 精度的能效为 196.6-102 TOPS/W/b。在MNIST、CIFAR10和CIFAR100上的推理结果显示,与软件基线相比,精度损失为$\leq 1\%$。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 2-to-10-b Output Precision Reconfigurable Compute-In-Memory Macro Leveraging Input Conditioning Using Residue Amplification
Artificial intelligence workloads demand a wide range of multiply and accumulate (MAC) precision. Pitch-matching constraints in compute-in-memory (CIM) engines limit the analog-to-digital converter (ADC) precision to about 8 bits. This letter demonstrates a method of mapping a suitable input conditioned MAC range to the input dynamic range of the on-chip 7-b ADC, thereby achieving up to 10 bits of output MAC precision. A 424 Kb SRAM CIM macro was fabricated in TSMC 28 nm, which computes 72 MACs in parallel per cycle. Measurement results at nominal supply voltage show an energy efficiency of 196.6–102 TOPS/W/b for a 2–10 bit output MAC precision. Inference results on MNIST, CIFAR10, and CIFAR100 are shown with $\leq 1\%$ accuracy loss from the software baseline.
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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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