[2603.19333] POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization

Computer Science > Hardware Architecture arXiv:2603.19333 (cs) [Submitted on 19 Mar 2026] Title:POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization Authors:Heng Ping, Peiyu Zhang, Zhenkun Wang, Shixuan Li, Anzhe Cheng, Wei Yang, Paul Bogdan, Shahin Nazarian View a PDF of the paper titled POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization, by Heng Ping and 7 other authors View PDF HTML (experimental) 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 ach...