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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2603.19333 |
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| _version_ | 1866908901790711808 |
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| author | Ping, Heng Zhang, Peiyu Wang, Zhenkun Li, Shixuan Cheng, Anzhe Yang, Wei Bogdan, Paul Nazarian, Shahin |
| author_facet | Ping, Heng Zhang, Peiyu Wang, Zhenkun Li, Shixuan Cheng, Anzhe Yang, Wei Bogdan, Paul Nazarian, Shahin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19333 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization Ping, Heng Zhang, Peiyu Wang, Zhenkun Li, Shixuan Cheng, Anzhe Yang, Wei Bogdan, Paul Nazarian, Shahin Hardware Architecture Artificial Intelligence 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. |
| title | POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization |
| topic | Hardware Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2603.19333 |