Compass: A Collaborative HLS Design Space Exploration Framework via Graph Representation Learning and Ensemble Bayesian Optimization

This paper presents Compass, an end-to-end design space exploration (DSE) framework to automate highlevel synthesis (HLS) directive optimization efficiently. Compass incorporates a directed acyclic graph (DAG) based modeling method which utilizes a customized variational graph autoencoder (VGAE) to transform the original discrete space to a continuous embedding space to improve the optimization efficiency. An active-learning-based sampling algorithm PRD-AL is proposed to draw high-quality initial samples. To enhance the robustness of Compass, we propose a novel MOEBO algorithm based on ensemble Bayesian optimization which constructs multiple local agents in trust regions and maintains a global agent to rectify the optimization trend. Besides, a collaborative acquisition maximization strategy is developed to encourage multiple agents to select promising candidates competitively. The experimental results demonstrate the superior performance of our approach to approximate the Pareto frontier with more than 51.18 % ADRS improvement and 22.20% PPA gain compared to state-of-the-art DSE approaches on different benchmarks.