MicroEvo: Knowledge-Guided LLM Sampling for Efficient Microarchitecture Design Space Exploration

2026-08-06

AI Summary

This work addresses the challenges of microarchitectural design space exploration, which is hindered by an enormous search space and high costs of power, performance, and area (PPA) evaluation. Traditional approaches struggle to model complex microarchitectural dependencies and suffer from low learning efficiency. To overcome these limitations, this study introduces, for the first time, a knowledge-guided large language model (LLM) integrated with Monte Carlo Tree Search (MCTS). The proposed framework features LLM-driven evolutionary operators, a tree policy that balances Pareto contribution and diversity, and mechanisms for state-aware prompting and active knowledge accumulation, enabling efficient multi-objective optimization. Experimental results on industrial-grade cores demonstrate that the method improves Pareto front quality by up to 36.2% and achieves a 10.6× speedup in search efficiency, exhibiting strong scalability.

Abstract

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.

Problem

• microarchitecture design space exploration
• PPA evaluation
• Pareto convergence
• search efficiency
• design optimization

Innovation

• LLM-guided optimization
• Monte Carlo Tree Search
• Pareto-aware search
• microarchitecture design space exploration
• knowledge accumulation