GA-Optimized Non-Uniform Quantization Scheme for Mitigating MAC Variations in ReRAM-Based Compute-in-Memory Architectures
Abstract
ReRAM-based Compute-in-Memory (CiM) architectures can accelerate AI workloads. However, device-level variations in ReRAM distort the distribution of multiply-accumulate (MAC) values, thereby degrading computation accuracy. This paper proposes a non-uniform quantization (NUQ) scheme optimized by genetic algorithms (GA) to reduce MAC readout errors while maintaining high computational efficiency. The proposed NUQ method adapts to resistance variations and signal aggregation effects. Under multi-weight and aging scenarios, it effectively mitigates signal overlap and improves readout accuracy by 60%.
Fields of Study & Subjects
Advanced Memory and Neural Computing
Low-power high-performance VLSI design
Ferroelectric and Negative Capacitance Devices
Publication details
- Journal / source
- IEICE Transactions on Electronics
- Volume
- E109.C
- Issue
- 9
- Pages
- 341–345
- Publication date
- 28 January 2026
- Language
- EN