Applying large language models (LLMs) to RTL code optimization for improved power, performance, and area (PPA) faces two key challenges: ensuring functional correctness of optimized designs despite LLM hallucination, and systematically prioritizing power reduction within the multi-objective PPA trade-off space. We propose POET (Power-Oriented Evolutionary Tuning), a framework that addresses both challenges. For functional correctness, POET introduces a differential-testing-based testbench generation pipeline that treats the original design as a functional oracle, using deterministic simulation to produce golden references and eliminating LLM hallucination from the verification process. For PPA optimization, POET employs an LLM-driven evolutionary mechanism with non-dominated sorting, power-first intra-level ranking, and proportional survivor selection to steer the search toward the low-power region of the Pareto front without manual weight tuning. Evaluated on the RTL-OPT benchmark across 40 diverse RTL designs, POET achieves 100% functional correctness, the best power on all 40 designs, and competitive area and delay improvements.

翻译：将大语言模型（LLM）应用于RTL代码优化以提升功耗、性能与面积（PPA）面临两大挑战：如何在LLM产生幻觉时确保优化设计的功能正确性，以及如何在多目标PPA权衡空间中系统性地优先进行功耗优化。本文提出POET（面向低功耗的进化调优框架），该框架同时解决上述挑战。在功能正确性方面，POET引入基于差异测试的测试平台生成流水线，将原始设计作为功能预言机，通过确定性仿真产生黄金参考结果，从而消除验证过程中LLM的幻觉影响。在PPA优化方面，POET采用LLM驱动的进化机制，结合非支配排序、功耗优先层级排序和比例幸存者选择，无需人工权重调优即可将搜索导向帕累托前沿的低功耗区域。在RTL-OPT基准测试的40个多样化RTL设计上的评估表明，POET实现了100%的功能正确性，在所有40个设计上均获得最优功耗，并在面积和时序方面取得有竞争力的改善效果。