Hybrid SRAM/ROM Compute-in-Memory Architecture for High Task-Level Energy Efficiency in Transformer Models With 8928-kb/mm² Density in 28nm CMOS

This article reports the first ultra-high-density hybrid static random access memory (SRAM)/read-only memory (ROM) compute-in-memory (CiM) architecture for multibit multiply-accumulate (MAC) operations of transformer models. It features several techniques that enhance the on-chip weight density, energy efficiency, and flexibility, namely: 1) the hybrid CiM structure of 6T SRAM and 1T multi-level cell (MLC) ROM for ultra-high memory density to improve the limited on-chip memory capacity and alleviate the frequent weight reload from dynamic random access memory (DRAM); 2) 2-/5-b adaptive-resolution analog-to-digital converters (ADCs) for energy reduction and low accuracy loss; and 3) post-fabrication 1-of-4 capacitor selection (PFCS) to reduce the capacitance of computing capacitors, which was originally limited by the capacitor variation. This leads to lower energy and higher accuracy. A record-high 22-Mb ROM CiM and 896-kb SRAM CiM macro has demonstrated the 8 × 8 b MAC operations of one transformer layer, featuring ultra-high weight density of 8928 kb/mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and significantly reduced DRAM access activities toward high inference energy efficiency.

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Publication Details

Journal
IEEE Journal of Solid-State Circuits
Published
2025-04-15
DOI
https://doi.org/10.1109/jssc.2025.3556008
Citations
6
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
4.38

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article

Hybrid SRAM/ROM Compute-in-Memory Architecture for High Task-Level Energy Efficiency in Transformer Models With 8928-kb/mm² Density in 28nm CMOS

Guodong Yin, Wenjun Tang, Huazhong Yang, Xirui Du et al.
6 citations
IEEE Journal of Solid-State Circuits
Advanced Memory and Neural Computing
4.38
article

Hybrid SRAM/ROM Compute-in-Memory Architecture for High Task-Level Energy Efficiency in Transformer Models With 8928-kb/mm² Density in 28nm CMOS

Guodong Yin, Wenjun Tang, Huazhong Yang, Xirui Du, Yongpan Liu, Mufeng Zhou, Jinshan Yue, Hongyang Jia, Mingyen Lee, Yiming Chen, Xueqing Li, K. Yue, Zhonghao Chen
article en
6 citations

Abstract

This article reports the first ultra-high-density hybrid static random access memory (SRAM)/read-only memory (ROM) compute-in-memory (CiM) architecture for multibit multiply-accumulate (MAC) operations of transformer models. It features several techniques that enhance the on-chip weight density, energy efficiency, and flexibility, namely: 1) the hybrid CiM structure of 6T SRAM and 1T multi-level cell (MLC) ROM for ultra-high memory density to improve the limited on-chip memory capacity and alleviate the frequent weight reload from dynamic random access memory (DRAM); 2) 2-/5-b adaptive-resolution analog-to-digital converters (ADCs) for energy reduction and low accuracy loss; and 3) post-fabrication 1-of-4 capacitor selection (PFCS) to reduce the capacitance of computing capacitors, which was originally limited by the capacitor variation. This leads to lower energy and higher accuracy. A record-high 22-Mb ROM CiM and 896-kb SRAM CiM macro has demonstrated the 8 × 8 b MAC operations of one transformer layer, featuring ultra-high weight density of 8928 kb/mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and significantly reduced DRAM access activities toward high inference energy efficiency.

IEEE Journal of Solid-State CircuitsVol. 60(11)
Chinese Academy of Sciences (CN), University Town of Shenzhen (CN), Institute of Microelectronics (CN), Tsinghua University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 6%
Advanced Memory and Neural Computing
4.38
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