Presentation
Agenticdse: A Multi-Agent Design Space Exploration Framework with Multi-Phase Bayesian Optimization for Chiplet Accelerators
DescriptionChiplet accelerators offer a scalable solution for LLM inference with reduced manufacturing costs. However, the design space exploration (DSE) of chiplet accelerators is challenging due to the complex design space. Prior black-box Bayesian optimization (BO) solutions lack domain knowledge, limiting their effectiveness. In this work, we propose AgenticDSE, a multi-agent DSE framework that incorporates the collaboration of three LLM agents, i.e., exploration orchestrator, architecture analyst, and optimization engineer. This multi-agent framework enhances exploration efficiency through analysis-driven design space refinement and phase-wise design exploration using ensemble surrogate modeling. Over the same number of explorations, AgenticDSE achieves up to 36.9% reduction of average distance to the real Pareto front, and 66% increase in diversity of explored Pareto front compared to state-of-the-art DSE solutions. Additionally, it offers scalable performance with 46x and 18x reduction in input and output token consumption compared to prior LLM-based solutions.
Event Type
Research Manuscript
TimeTuesday, July 282:08pm - 2:21pm PDT
LocationMtg Room 201B