Low-Rank Compensation in Hybrid 3D-RRAM/SRAM Computing-in-Memory System for Edge Computing

Abstract

Artificial intelligence (AI) has made significant strides, with computing-in-memory (CIM) emerging as a key enabler for energy-efficient AI acceleration. Resistive random-access memory (RRAM)-based analog CIM offers better energy efficiency and storage density compared to static random-access memory (SRAM)-based digital CIM. Building on this, three-dimensional (3D) RRAM further improves storage density through vertical stacking. However, 3D-RRAM-CIM is susceptible to variation, which degrades accuracy and poses a significant challenge for system-level deployment in edge computing. Furthermore, the constrained capacity of CIM limits the multitasking performance. In this work, low-rank adaptation is applied to the Hybrid CIM system (Hybrid-CIM) for the first time, which leverages high-density 3D RRAM and high-precision SRAM, to address these challenges. Simulation results illustrate the feasibility of our approach, reducing accuracy degradation by 86% and achieving an 8.5× reduction in area with less than 2% weight overhead. In ResNet-18, with the backbone stored in 3D-RRAM kept fixed, the proposed low-rank adaptation branch (LoBranch) approach achieves an accuracy of 94.0% on CIFAR-10, which is only 0.4% lower than the noise-free digital baseline. This work strikes a favorable balance between accuracy and area, thereby facilitating reliable and efficient 3D-RRAM-based edge computing.