MicroEvo用LLM引导芯片微架构设计搜索，比NSGA-II质量高36.2%，效率提升10倍多，代码已开源。
论文提出MicroEvo框架，将现成LLM与蒙特卡洛树搜索（MCTS）结合用于多目标微架构优化。该框架包含LLM驱动的进化算子、Pareto感知树策略、主动知识积累机制和状态感知指令。实验显示，MicroEvo相比NSGA-II将Pareto前沿质量最高提升36.2%，搜索效率提升10.6倍。该方法在复杂工业级核心上展现出强可扩展性，代码已在GitHub开源。
MicroEvo: Knowledge-Guided LLM Sampling for Efficient Microarchitecture Design Space Exploration
Microarchitecture design space exploration suffers from expansive search spaces and expensive PPA evaluation, leaving only a small simulation budget for design decision-making. Existing methods perform blind search without considering microarchitectural dependencies and fail to learn from the iterative search effectively, leading to wasted evaluations and weak Pareto convergence. In this paper, we propose MicroEvo, a knowledge-guided framework that couples off-the-shelf LLMs with Monte Carlo Tree Search (MCTS) for multi-objective microarchitecture optimization. MicroEvo combines LLM-driven evolutionary operators, a Pareto-aware tree policy that balances Pareto contribution and diversity, an active knowledge accumulation mechanism that extracts and reuses optimization insights, and state-aware directives that adapt the search behavior online. Experiments show that MicroEvo improves Pareto-front quality by up to 36.2% over NSGA-II and achieves 10.6x higher search efficiency, and also demonstrates strong scalability to a complex industrial-scale core. The code repository is available at: https://github.com/GEAR-SEU/MicroEvo-ICCAD-26.