ARXIV:2601.18067 · AI FOR ENGINEERING · SUBMITTED 17 MAR · 21:43 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

EvolVE: Evolutionary Search for LLM-based Verilog Generation and Optimization

arXiv

EvolVE uses evolutionary algorithms to optimize Verilog generation, significantly improving hardware design efficiency.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain EvolVE uses evolutionary algorithms to optimize Verilog generation, significantly improving hardware design efficiency.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

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PROBLEM

EvolVE uses evolutionary algorithms to optimize Verilog generation, significantly improving hardware design efficiency. Although Large Language Models (LLMs) offer a promising pathway toward automation, their limited training data and intrinsic sequential reasoning fail to…

METHOD

Full abstract

Verilog's design cycle is inherently labor-intensive and necessitates extensive domain expertise. Although Large Language Models (LLMs) offer a promising pathway toward automation, their limited training data and intrinsic sequential reasoning fail to capture the strict formal logic and concurrency inherent in hardware systems. To overcome these barriers, we present EvolVE, the first framework to analyze multiple evolution strategies on chip design tasks, revealing that Monte Carlo Tree Search (MCTS) excels at maximizing functional correctness, while Idea-Guided Refinement (IGR) proves superior for optimization. We further leverage Structured Testbench Generation (STG) to accelerate the evolutionary process. To address the lack of complex optimization benchmarks, we introduce IC-RTL, targeting industry-scale problems derived from the National Integrated Circuit Contest. Evaluations establish EvolVE as the new state-of-the-art, achieving 98.1% on VerilogEval v2 and 92% on RTLLM v2. Furthermore, on the industry-scale IC-RTL suite, our framework surpasses reference implementations authored by contest participants, reducing the Power, Performance, Area (PPA) product by up to 66% in Huffman Coding and 17% in the geometric mean across all problems. The source code of the IC-RTL benchmark is available at https://github.com/weiber2002/ICRTL.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. The source code of the IC-RTL benchmark is available at https://github.com/weiber2002/ICRTL.

WHY NOW

AI for Engineering moved forward this cycle; last verified April 2026. Public score 8.0/10.

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Opportunity summary

Score8.0

PainEvolVE uses evolutionary algorithms to optimize Verilog generation, significantly improving hardware design efficiency.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

EvolVE uses evolutionary algorithms to optimize Verilog generation, significantly improving hardware design efficiency.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

EvolVE uses evolutionary algorithms to optimize Verilog generation, significantly improving hardware design efficiency.

Segment

AI for Engineering

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

EvolVE uses evolutionary algorithms to optimize Verilog generation, significantly improving hardware design efficiency.

Segment

AI for Engineering

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

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not classified

Adjacent

not classified

Substitute

not classified

Unknown

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EvolVE: Evolutionary Search for LLM-based Verilog Generation and Optimization

EvolVE: Evolutionary Search for LLM-based Verilog Generation and Optimization

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