Researchllmrtl optimizationevolutionary algorithms
POET Optimizes RTL Designs For Lower Power
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7.6
Researchers introduce POET, a framework using large language models to optimize RTL code for power, performance, and area, submitted Mar 19, 2026. POET uses differential-testing testbench generation to guarantee functional correctness and an LLM-driven evolutionary search with non-dominated sorting and power-first ranking to prioritize power. Evaluated on the RTL-OPT benchmark across 40 designs, it achieves 100% functional correctness and best power on all designs.
Scoring Rationale
Strong empirical results and novel LLM-based RTL optimization; limited by single arXiv submission and domain-specific scope.
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