Sensitivity-Aware Mixed-Precision Quantization for ReRAM-based Computing-in-Memory

By Guan-Cheng Chen ¹, Chieh-Lin Tsai ², Pei-Hsuan Tsai  ¹, Yuan-Hao Chang ^2
1 National Cheng Kung University, Tainan, Taiwan
² National Taiwan University, Taipei, Taiwan

Abstract

Compute-In-Memory (CIM) systems, particularly those utilizing ReRAM and memristive technologies, offer a promising path toward energy-efficient neural network computation. However, conventional quantization and compression techniques often fail to fully optimize performance and efficiency in these architectures. In this work, we present a structured quantization method that combines sensitivity analysis with mixed-precision strategies to enhance weight storage and computational performance on ReRAM-based CIM systems. Our approach improves ReRAM Crossbar utilization, significantly reducing power consumption, latency, and computational load, while maintaining high accuracy. Experimental results show 86.33% accuracy at 70% compression, alongside a 40% reduction in power consumption, demonstrating the method's effectiveness for power-constrained applications.

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