Large Language Models (LLMs) have shown promising progress for generating Register Transfer Level (RTL) hardware designs, largely because they can rapidly propose alternative architectural realizations. However, single-shot LLM generation struggles to consistently produce designs that are both functionally correct and power-efficient. This paper proposes HYPERHEURIST, a simulated annealing-based control framework that treats LLM-generated RTL as intermediate candidates rather than final designs. The suggested system not only focuses on functionality correctness but also on Power-Performance-Area (PPA) optimization. In the first phase, RTL candidates are filtered through compilation, structural checks, and simulation to identify functionally valid designs. PPA optimization is restricted to RTL designs that have already passed compilation and simulation. Evaluated across eight RTL benchmarks, this staged approach yields more stable and repeatable optimization behavior than single-pass LLM-generated RTL.

翻译：大型语言模型（LLMs）在生成寄存器传输级（RTL）硬件设计方面展现出显著进展，这主要源于其能够快速提出多种架构实现方案。然而，单次LLM生成难以持续产出既功能正确又功耗优化的设计。本文提出HYPERHEURIST——一种基于模拟退火的控制框架，将LLM生成的RTL视为中间候选方案而非最终设计。该系统不仅关注功能正确性，还着重优化功耗-性能-面积（PPA）。在第一阶段，RTL候选方案需通过编译检查、结构验证和仿真筛选，以识别功能有效的设计。PPA优化仅针对已通过编译和仿真的RTL设计实施。基于八项RTL基准测试的评估表明，这种分阶段方法相比单次LLM生成的RTL，能够实现更稳定且可重复的优化行为。