LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration

Abstract:Chiplet technology is a key approach for extending Moore's Law. However, its design process is constrained by two primary challenges: the scarcity of early-stage platforms for rapidly evaluating chiplet-based heterogeneous systems, and the complex relationships between design parameters and optimization metrics. To address these challenges, we propose a synergistic framework that integrates a Large Language Model (LLM) with Bayesian Optimization (BO), enabling architectural-level simulation and automated optimization for the 2.5D/3D CPU-GPU heterogeneous chiplets. We extend the open-source chiplet simulator to evaluate 3D designs by supporting graph topologies. Further, to enhance the exploration efficiency of BO, we introduce an LLM that generates high-quality policies to guide the optimization process, enabled by our proposed adaptive directional policy reasoning (ADPR) and an exploration gain-aware fine-tuning method. Finally, we use the policy-guided BO to perform an automated and efficient exploration of the complex chiplet design space. Experiments on 2.5D and 3D chiplet reveal that our framework improves design space exploration efficiency.

Machine Learning (cs.LG)

arXiv:2610.14612 [cs.LG]

(or arXiv:2610.14612v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.14612 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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