POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization

• URL: http://arxiv.org/abs/2603.19333v1
• Date: Thu, 19 Mar 2026 03:19:32 GMT
• Title: POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization
• Abstract summary: We propose POET (Power-Oriented Evolutionary Tuning), a framework that addresses both challenges.<n>For functional correctness, POET introduces a differential-testing-based testbench generation pipeline that treats the original design as a functional.<n>For PPA optimization, POET employs an LLM-driven evolutionary mechanism with non-dominated sorting, power-first intra-level ranking, and proportional survivor selection.
• Score: 15.350025051608064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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