Accuracy Improvement With Weight Mapping Strategy and Output Transformation for STT-MRAM-Based Computing-in-Memory

IF 2 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Xianggao Wang;Na Wei;Shifan Gao;Wenhao Wu;Yi Zhao
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引用次数: 0

Abstract

This work presents an analog computing-in-memory (CiM) macro with spin-transfer torque magnetic random access memory (STT-MRAM) and 28-nm CMOS technology. The adopted CiM bitcell uses a differential scheme and balances the input resistance to minimize the nonideal factors during multiply-accumulate (MAC) operations. Specialized peripheral circuits were designed for the current-scheme CiM architecture. More importantly, strategies of accuracy improvement were innovatively proposed as follows: 1) mapping most significant bit (MSB) to the far side of the MRAM array and 2) output linear transformation based on the reference columns. Circuit-level simulation verified the functionality and performance improvement of the CiM macro based on the MNIST and CIFAR-10 datasets, realizing a 3% and 5% accuracy loss compared with the benchmark, respectively. The 640-GOPS (8 bit) throughput, 34.6-TOPS/mm2 area compactness, and 83.3-TOPS/W energy efficiency demonstrate the advantages of STT-MRAM CiM in the coming AI era.
利用权重映射策略和输出变换提高基于 STT-MRAM 的内存计算精度
本研究采用自旋转移力矩磁性随机存取存储器(STT-MRAM)和 28 纳米 CMOS 技术,提出了一种模拟计算内存(CiM)宏。所采用的 CiM 位元组使用差分方案并平衡输入电阻,以最大限度地减少乘积 (MAC) 运算过程中的非理想因素。针对电流方案 CiM 架构设计了专用外围电路。更重要的是,创新性地提出了以下提高精度的策略:1) 将最重要位 (MSB) 映射到 MRAM 阵列的远端;2) 基于参考列的输出线性变换。电路级仿真验证了基于 MNIST 和 CIFAR-10 数据集的 CiM 宏的功能和性能改进,与基准相比分别实现了 3% 和 5% 的精度损失。640-GOPS(8 位)的吞吐量、34.6-TOPS/mm2 的面积紧凑性和 83.3-TOPS/W 的能效证明了 STT-MRAM CiM 在即将到来的人工智能时代的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.00
自引率
4.20%
发文量
11
审稿时长
13 weeks
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