HYPERHEURIST: A Simulated Annealing-Based Control Framework for LLM-Driven Code Generation in Optimized Hardware Design

HYPERHEURIST introduces a simulated annealing control framework that enhances LLM-generated hardware design by treating outputs as optimization candidates rather than final products. The system combines functional validation through compilation and simulation with Power-Performance-Area optimization, demonstrating more stable results than single-pass LLM generation across eight benchmarks.

This research addresses a critical limitation in AI-assisted hardware design: while LLMs excel at rapidly generating architectural alternatives, their outputs frequently lack the functional correctness and efficiency required for production systems. HYPERHEURIST bridges this gap by implementing a staged validation pipeline that separates concerns—first ensuring designs compile and simulate correctly, then optimizing for power, performance, and area constraints.

The approach reflects broader trends in AI engineering where language models serve as creative tools within constrained optimization frameworks rather than autonomous generators. Hardware design represents an exceptionally demanding application domain where functional correctness is non-negotiable and marginal efficiency gains translate to substantial cost savings in manufacturing and operations. The research builds on established techniques in combinatorial optimization (simulated annealing) applied to a novel problem domain.

For the hardware and semiconductor industry, this work demonstrates pathways toward accelerating design cycles while maintaining quality standards. Design iterations currently consume significant time and resources; systematic use of LLMs within validation frameworks could reduce time-to-market for complex chips. The emphasis on PPA optimization directly impacts competitive positioning, as power efficiency increasingly determines commercial viability in cloud computing, mobile, and edge device markets.

The staged validation approach suggests future frameworks may employ similar multi-phase architectures where LLMs generate candidates that human engineers or automated systems progressively refine. Broader implications extend to any domain requiring both creative generation and rigorous validation—compiler optimization, network architecture design, and robotics control systems could adopt comparable methodologies.

• →LLM-generated RTL designs require systematic validation and optimization rather than direct deployment as final products.
• →Staged filtering through compilation, structural checks, and simulation ensures functional correctness before optimization begins.
• →Simulated annealing control frameworks enable consistent Power-Performance-Area optimization across diverse hardware benchmarks.
• →The approach demonstrates more stable and repeatable results compared to single-pass LLM generation methods.
• →Hardware design represents a high-stakes application domain where marginal efficiency improvements justify sophisticated optimization pipelines.