发表机构
Institute of Computing Technology, CAS; University of Chinese Academy of Sciences; CASTEST Co., Ltd.; Department of Computer Science, Peking University（中国科学院计算技术研究所; 中国科学院大学; 芯华章实业有限公司; 北京大学计算机科学与技术系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有结构化近似逻辑综合忽视结构偏差的问题，提出E-ALS框架，利用E图重写识别近似友好的等效结构，在最大汉明距离和误差距离约束下分别额外减少面积3.2和7.0个百分点。
AI 中文摘要
近似逻辑综合（ALS）通过以有界功能误差换取电路功耗、性能和面积的改善。然而，现有的结构化ALS方法在很大程度上忽视了结构偏差：即使功能等效的网表也可能展现出截然不同的近似机会，并在相同的下游ALS流程下导致显著不同的结果。我们的实验表明，这种效应可导致最终面积差距高达42.77%。为发掘这一机会，我们提出E-ALS，一种基于E图的近似感知结构搜索框架。E-ALS结合了函数缩减饱和、ALS耦合替代、基于搜索的提取和预算引导的细化，以识别近似友好的等效结构。在成熟的算术和逻辑基准上的实验表明，在最大汉明距离和误差距离约束下，E-ALS分别实现了额外的面积减少3.2和7.0个百分点。代码可在以下URL获取。
英文摘要
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.
Comments9 pages, 11 figures. Accepted at ICCAD 2026
