A 28-nm RRAM/SRAM Collaborative CIM Accelerator Supporting RRAM-Endurance-Latency Awareness for Edge Fine-Tuning

The resistive random access memory (RRAM)-based computing-in-memory features high density and high energy efficiency on edge. However, fine-tuning RRAM-based SoCs remains challenging due to the inherent limitations of non-volatile memory (NVM) characteristics. This work proposes an NVM-endurance/latency-aware collaborative RRAM/static random access memory (SRAM) compute-in-memory (CIM) accelerator that addresses the difficulties associated with RRAM write operations during fine-tuning tasks for both CNN and transformer models. The major contributions are: 1) RRAM-most significant bit (MSB)–SRAM-least significant bit (LSB) (RMSL)-based collaborative CIM macros, mitigating RRAM cell flipping times and alleviating the endurance concern; 2) an RRAM-sparse-SRAM-dense (RSSD) weight updating engine, minimizing the long reading and writing latency associated with RRAM access; and 3) a row-wise pipeline weight gradient (WG) computing data flow with low-hardware overhead. With a maximum bit update of ten times per fine-tuning (20 epochs, from start to convergence), the system achieves an energy efficiency of 76.25 TOPS/W for the CIM macro and 22.07 TOPS/W for the overall system. Thanks to the bit-level collaborative CIM, the RRAM CIM macro achieves the same hardware utilization during fine-tuning and inference processes to support RRAM’s energy-efficient computing. The proposed CIM accelerator, fabricated using 28-nm CMOS technology, achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$143{\times }$</tex-math> </inline-formula> RRAM endurance improvement, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$117{\times }$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$144{\times }$</tex-math> </inline-formula> reduction in RRAM write power and latency.

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Publication Details

Journal
IEEE Journal of Solid-State Circuits
Published
2025-06-17
DOI
https://doi.org/10.1109/jssc.2025.3577335
Citations
4
Primary Topic
VLSI and Analog Circuit Testing
Type
article
Field-Weighted Citation Impact
9.08

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article

A 28-nm RRAM/SRAM Collaborative CIM Accelerator Supporting RRAM-Endurance-Latency Awareness for Edge Fine-Tuning

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IEEE Journal of Solid-State Circuits
VLSI and Analog Circuit Testing
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article

A 28-nm RRAM/SRAM Collaborative CIM Accelerator Supporting RRAM-Endurance-Latency Awareness for Edge Fine-Tuning

Liang Chen, Yuliang Zhou, Jie Liao, Haozhe Zhu, Jianguo Yang, Chixiao Chen, Chen Mu, Yechu Zhang, Qi Liu, H. W. Jiang, Z.-X. Huang
article en
4 citations

Abstract

The resistive random access memory (RRAM)-based computing-in-memory features high density and high energy efficiency on edge. However, fine-tuning RRAM-based SoCs remains challenging due to the inherent limitations of non-volatile memory (NVM) characteristics. This work proposes an NVM-endurance/latency-aware collaborative RRAM/static random access memory (SRAM) compute-in-memory (CIM) accelerator that addresses the difficulties associated with RRAM write operations during fine-tuning tasks for both CNN and transformer models. The major contributions are: 1) RRAM-most significant bit (MSB)–SRAM-least significant bit (LSB) (RMSL)-based collaborative CIM macros, mitigating RRAM cell flipping times and alleviating the endurance concern; 2) an RRAM-sparse-SRAM-dense (RSSD) weight updating engine, minimizing the long reading and writing latency associated with RRAM access; and 3) a row-wise pipeline weight gradient (WG) computing data flow with low-hardware overhead. With a maximum bit update of ten times per fine-tuning (20 epochs, from start to convergence), the system achieves an energy efficiency of 76.25 TOPS/W for the CIM macro and 22.07 TOPS/W for the overall system. Thanks to the bit-level collaborative CIM, the RRAM CIM macro achieves the same hardware utilization during fine-tuning and inference processes to support RRAM’s energy-efficient computing. The proposed CIM accelerator, fabricated using 28-nm CMOS technology, achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$143{\times }$</tex-math> </inline-formula> RRAM endurance improvement, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$117{\times }$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$144{\times }$</tex-math> </inline-formula> reduction in RRAM write power and latency.

IEEE Journal of Solid-State CircuitsVol. 60(10)
Fudan University (CN), Shanghai Zhangjiang Laboratory (CN)
National Natural Science Foundation of China, Hellenic Foundation for Research and Innovation
Openalex Percentile: Top 2%
VLSI and Analog Circuit Testing
9.08
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