Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators

This paper presents a tool for automatically exploring the design space of deep learning accelerators (DLAs). Our main advancement is Starlight, a data-driven performance model that uses transfer learning to bridge the gap between fast, low-fidelity evaluation methods (such as analytical models) and slow, high-fidelity evaluation methods (such as RTL simulation). Starlight is fast: It can provide 6,500 predictions per second, allowing the evaluation of millions of configurations per hour. Starlight is accurate: It predicts the energy-delay product measured by RTL simulation with 99\% accuracy. And Starlight can be trained efficiently: It can be trained with 61\% fewer samples than DOSA's state-of-the-art data-driven performance predictor. Our second contribution is Polaris, a design-space exploration tool that uses Starlight to efficiently search the large, complex hardware/software co-design space of DLAs. In under 35 minutes, Polaris produces DLA designs that match the performance of designs that take six hours to produce with DOSA. And in under 3.3 hours, Polaris produces DLA designs that reduce energy-delay product by 2.7$\times$ over the best designs found by DOSA.