Leveraging Domain Information for the Efficient Automated Design of Deep Learning Accelerators

Deep learning accelerators are important tools for feeding the growing demand for deep learning applications. The automated design of such accelerators-which is important for reducing development costs-can be viewed as a search over a vast and complex design space that consists of all possible accelerators and all the possible software that could run on them.

Unfortunately, this search is complicated by the existence of many ordinal and categorical values, which are critical to explore for the ultimate design but are not handled well by existing search techniques.

This paper presents a technique for efficiently searching this space by injecting domain information-in this case information about hardware/software (HW/SW) co-design-into the automated search process. Specifically, this paper introduces a novel Bayesian optimization framework called daBO (domain-aware BO) that accepts domain information as input, including those describing ordinal and categorical values.

This paper also introduces Spotlight, a design tool based on daBO, and this paper empirically shows that Spotlight produces accelerator designs and software schedules that are orders of magnitude better than those created by the state-of-the-art. For example, for the ResNet-50 deep learning model, Spotlight produces a HW/SW configuration that reduces delay by 135× over the configuration produced by ConfuciuX, a state-of-theart HW/SW co-design tool, and Spotlight reduces energy-delay product (EDP) by 44× over an Eyeriss-like accelerator, which is an edge-scale hand-designed accelerator. In the realm of cloud-scale accelerators, Spotlight reduces the EDP of a scaledup Eyeriss-like accelerator by 23×. Our evaluation shows that Spotlight benefits from the efficiency of daBO, which allows Spotlight to identify accelerator designs and software schedules that prior work cannot identify.