AI 中文总结
研究逻辑优化运算符，利用智能源分析制定运算符关系实现理论驱动的运算符压缩，形成TACO，实验表明其在减少运行时、节点数和层级等方面表现出色，TACO - max在特定输入行上有较好的NDP几何平均比率。
AI 中文摘要
逻辑综合已从紧凑的两级最小化发展到具有许多相互作用的优化运算符的大型多级流程。近期工作在对这些运算符进行排序方面投入了大量精力。然而，运算符词汇本身是一个核心障碍。我们通过智能源分析来解决这一差距，利用大语言模型智能体从固定的ABC和mockturtle实现中制定运算符级关系，并通过对抗性审计来测试其规定范围。由此产生的经过验证的关系实现了理论驱动的运算符压缩。我们将这些门直接集成到ABC Orchestrate中形成TACO。实验表明，两个精确门在66个电路上使Orchestrate运行时减少11%且输出相同，TACO在多个方面表现更优，TACO - max在HeLO的三个精确输入行上实现了0.903的NDP几何平均比率。
英文摘要
Logic synthesis has evolved from compact two-level minimization to large multilevel flows with many interacting optimization operators. Recent work has invested substantial effort in sequencing these operators: actions are commonly treated as opaque choices in a rapidly expanding search space, while learned circuit representations and heuristic or local-greedy orchestration provide increasingly informed ways to explore it. A central obstacle is the operator vocabulary itself. Production operators are numerous, span different representations and mathematical foundations, and expose behaviors determined by implementation-level guards, bounds, and update order. We address this gap through agentic source analysis, using LLM agents to formulate operator-level relations from pinned ABC and mockturtle implementations and adversarial audits to test their stated scope. The resulting certified relations yield theory-derived operator compression: 40 deployed recipe actions collapse to a 31-action exact Pareto cover, and source-level conditions compile into deterministic admission gates. We integrate these gates directly into ABC Orchestrate to form TACO. Two exact gates reduce Orchestrate runtime by 11% with bit-identical outputs on 66 circuits. In a held-fixed integrated comparison, TACO uses fewer nodes on 14 of 16 circuits, with geometric-mean reductions of 1.0% in nodes and 3.2% in levels, while running 2.6x faster. TACO-max achieves an NDP geometric-mean ratio of 0.903 on HeLO's three exact-input rows.
Comments23 pages, 4 figures. Code and data: https://github.com/CODA-Team/TACO
