APPLE-DSE: Asynchronous Parallel Pareto Set Learning for Microarchitecture Design Space Exploration

The synthesizable and parameterizable RISC-V microarchitecture, combined with multiobjective optimization-based design space exploration (DSE), facilitates agile adaptation to various microprocessor designs for customized applications. However, to enhance design quality, DSE must consider both architecture parameters and EDA tool parameters, resulting in exponentially increased optimization complexity with the dimensionality of parameters. Exhaustively exploring the whole design space is impossible. Additionally, due to the time-consuming nature of microprocessor simulation, minimizing the number of simulations is imperative. Addressing these challenges, we propose asynchronous parallel Pareto set learning for microarchitecture DSE (APPLE-DSE). APPLE-DSE utilizes the Pareto set learning (PSL) technique to obtain an approximate Pareto front with a “light-weight” evaluation. PSL captures the structural characteristics of the Pareto set (PS) guided by the surrogate models, enabling it to explore any tradeoff area in the approximate PS. Employing the probabilistic reparameterization (PR) technique, APPLE-DSE adapts PSL to handle discrete variables. Furthermore, APPLE-DSE incorporates a simulation time-aware asynchronous parallel scheduling strategy to further enhance optimization efficiency. Experimental results show that APPLE-DSE achieves a maximum improvement of 16.81% in hypervolume within the same time budget and a $127.73\times $ speedup in algorithm run time per iteration compared to state-of-the-art methods.