T-MSA: Transformer-Driven Multi-Strategy Adaptive Microarchitecture Design Space Exploration

Le résumé fourni par la source

The design of modern processors ignores the topological relationships among all design parameters, leading to significant simulation costs wasted on invalid designs. Therefore, we propose the T-MSA to address this issue. It is a Transformer-driven multi-strategy adaptive design space exploration scheme. A customized lightweight Transformer (LiteFormer) is devised to model topological relationships among arbitrary design parameters, constructing an implicit interaction graph in the latent space. Secondly, we design a dynamic active learning (DynamicAL) strategy to extract sparse and high-quality initial points via sparse centroid initialization and hybrid sampling. Finally, a triple Pareto frontier acquisition function (TriPFAF) is devised to guide optimization direction based on gains from three types of Pareto frontiers, dynamically balancing exploration and exploitation. We conducted rigorous experiments on two BOOM evaluation platforms, demonstrating that T-MSA efficiently and comprehensively optimizes the performance-power-area (PPA) objective. The designs it identifies achieve significant improvements over state-of-the-art DSE algorithms on Pareto hypervolume (HV). When attaining the same HV value, T-MSA outperforms BOOM-Explorer by 188.24% and 133.33% on two platforms.

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