基于 14 纳米 MRAM 的多比特模拟内存计算,可对 72 个基于宏的加速器进行过程变化校准

IF 2.2 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Sungmeen Myung;Seok-Ju Yun;Minje Kim;Wooseok Yi;Jaehyuk Lee;Jangho An;Kyoung-Rog Lee;Chang-Woo Shin;Seungchul Jung;Soonwan Kwon
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引用次数: 0

摘要

本文介绍了一种利用 14 纳米 MRAM 技术的模拟内存计算 (IMC) 宏。为了促进高能效、高吞吐量的乘法累加(MAC)操作,采用堆叠式磁隧道结架构引入了多位权重,并提出了模拟位并行 MAC(ABP-MAC)方案。与最先进的基于 MRAM 的 IMC 宏相比,这种方法的 TOPS/mm2 性能提高了 3.3 倍。此外,综合校准技术显著提高了 72 个 IMC 宏的计算精度。所提出的 IMC 宏在 1 位归一化的情况下达到了 18.29 TOPS/mm2 和 340.8 TOPS/W,在谷歌语音命令数据集上的分类准确率为 90.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 14 nm MRAM-Based Multi-bit Analog In-Memory Computing With Process-Variation Calibration for 72 Macros-Based Accelerator
This letter presents an analog in-memory computing (IMC) macro utilizing 14 nm MRAM technology. To facilitate energy-efficient high-throughput multiply accumulate (MAC) operations, a multi-bit weight is introduced using stacked magnetic tunnel junction architecture and an analog bit-parallel MAC (ABP-MAC) scheme is proposed. This approach delivers 3.3 times better TOPS/mm2 than the state-of-the-art MRAM-based IMC macro. Additionally, a comprehensive calibration technique significantly improves computational accuracy across 72 IMC macros. The proposed IMC macro achieves 18.29 TOPS/mm2 and 340.8 TOPS/W with 1-bit normalization and classification accuracy of 90.2% with the Google speech commands dataset.
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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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