LEMOE: LLM-Enhanced Multi-Objective Bayesian Optimization for Microarchitecture Exploration

Designing processor microarchitectures is increasingly challenging due to a vast design space and the need to balance multiple metrics. Traditional algorithm-driven design space exploration (DSE) approaches often struggle to incorporate the extensive domain knowledge of expert architects. To address this, we introduce LEMOE, a multi-objective microarchitecture optimization framework that leverages large language model (LLM) to enhance an implicit Bayesian model. LEMOE features a program-aware warm-up phase utilizing LLM and LLVM to produce an initial design set with rich prior knowledge. By harnessing LLM’s contextual learning, our approach improves surrogate modeling and sampling under sparse data conditions. Experiment results show that LEMOE achieves a $22.8 \%$ improvement in energy efficiency with the same number of iterations and a $2.9 \times$ runtime speedup for the same target compared to prior works.