Swift or Exact? Boosting Efficient Microarchitecture DSE via Multi-fidelity Partial Order Prediction

A significant challenge in microarchitecture design space exploration (DSE) lies in the time-intensive synthesis and simulation process, making rapid design exploration infeasible. While the simulation tools offer reports on performance, power, and area (PPA) in the different stages, the PPA reports at early stages may fail to reflect the true relative qualities for various designs, i.e., with low fidelities. To address these limitations, we propose a novel multi-fidelity optimization algorithm tailored for multi-stage optimization problems. The proposed method employs a non-linear Gaussian process model to effectively fuse data from different stages with different fidelities, minimizing the need for expensive high-fidelity data while maximizing accuracy. Furthermore, a logical regression function and a multi-objective partial order relation are introduced to evaluate the reliability of low-fidelity data, mitigating their potential inaccuracies. Experiments demonstrate that our proposed multi-fidelity optimization algorithm can approximate the Pareto front of the direct design space in a shorter time with better performance.