OpenOpt: An Open-Source SRAM Optimizer Based on Equivalent Circuit Model
2026-06-08 • Neural and Evolutionary Computing
Neural and Evolutionary ComputingHardware Architecture
AI summaryⓘ
The authors created a new method to improve the design of SRAM memory by optimizing both its overall structure and the size of its tiny transistors at the same time. They made the simulation much faster by simplifying inactive parts, without losing much accuracy. They tested seven different algorithms and found that one called MOEA/D worked best, making the memory smaller, more reliable, and using less power. Their approach and code are openly shared for others to use.
SRAMtransistor sizingequivalent circuit modelssimulation speedupmulti-objective optimizationMOEA/DFreePDK45static noise marginpower reduction
Authors
Yikai Wang, Yiheng Wu, Can Wang, Bohao Liu, Junhao Ma, Zhuohua Liu, Qinxin Mei, Shan Shen
Abstract
This paper proposes a co-optimization framework that jointly optimizes SRAM architecture and transistor sizing using equivalent circuit models. The framework simplifies inactive SRAM cells into equivalent RC loads and static power models, achieving up to 61.4$\times$ simulation speedup while maintaining high fidelity (read/write delay error $<$0.22%, power error $<$1.68%). A joint search space encompassing architecture parameters and device sizing integrates seven algorithms including SA, PSO, Bayesian Optimization variants, and multi-objective evolutionary algorithms. Based on FreePDK45, ablation experiments confirm complementary gains from architecture selection and transistor sizing. Among all algorithms, MOEA/D achieves the best Figure of Merit (8.2721), yielding 6.2% improvement in SNM, 73.6% reduction in area, and 42.3% reduction in peak power. The framework is publicly available at https://github.com/W1Y1K1/OpenOpt.