ARS-Flow: A Design Space Exploration Flow for Accelerator-rich System based on Active Learning

Surrogate model-based design space exploration (DSE) is the mainstream method to search for optimal microarchitecture designs. However, it is hard to build accurate models for accelerator-rich systems within limited samples due to its high dimensional characteristic. Moreover, it is easy to fall into local optimal or difficult to converge. To solve these two problems, we propose a DSE flow based on active learning, namely ARS-Flow. It is featured with Pareto-region-oriented stochastic resampling method (PRSRS) and multiobjective genetic algorithm with self-adaptive hyperparameter control (SAMOGA). Taking the gem5-SALAM system for illustration, the proposed method can build more accurate models and find better microarchitecture designs with acceptable runtime costs.