E-ALS: Approximation-Potential-Aware E-Graph Rewriting for Approximate Logic Synthesis

2026-09-07

AI Summary

为解决近似逻辑综合中的结构偏差问题，提出E-ALS框架，通过e-图重写等方法识别利于近似的等效结构，实现更优的面积减少。

Abstract

Approximate logic synthesis (ALS) improves circuit power, performance, and area by trading exact correctness for bounded functional error. However, existing structural ALS methods largely overlook structural bias: even functionally equivalent netlists can expose markedly different approximation opportunities and lead to substantially different outcomes under the same downstream ALS flow. Our experiments show that this effect can induce final area gaps of up to 42.77%. To unlock this opportunity, we propose E-ALS, an e-graph-based framework for approximation-aware structural search. E-ALS combines Function-Reduced Saturation, an ALS-coupled surrogate, search-based extraction, and budget-guided refinement to identify approximation-friendly equivalent structures. Experiments on well-established arithmetic and logic benchmarks show that E-ALS achieves additional area reductions of 3.2 and 7.0 percentage points under maximum Hamming-Distance and Error-Distance constraints, respectively. Code is available in https://github.com/ZenuSunB/Ecompile.git.

Problem

• approximate logic synthesis
• structural bias
• functional error

Innovation

• E-Graph
• Approximate Logic Synthesis
• Function-Reduced Saturation
• Budget-Guided Refinement