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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 135x over the configuration produced by ConfuciuX, a state-of-the-art HW/SW co-design tool, and Spotlight reduces energy-delay product (EDP) by 44x 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 scaled-up Eyeriss-like accelerator by 23x. 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. 

State-of-the-art value predictors either use control-flow context or data context to predict values. Predictors based on control-flow context use branch histories to remember past values, but these predictors require lengthy histories to predict anything other than constant and strided values. Predictors that use data context---also known as Finite Context Method (FCM) predictors---use a history of past values to predict a broader class of values, but such predictors achieve low coverage due to long training times, and they can become complex due to speculative value histories. We observe that the combination of branch and value history provides better predictability than the use of each history separately because it can predict values in control-dependent sequences of values. Furthermore, the combination improves training time by enabling accurate predictions to be made with shorter history, and it simplifies the hardware design by removing the need for speculative value histories. Based on these observations, we propose a new unlimited budget value predictor, Heterogeneous-Context Value Predictor (HCVP), that when hybridized with E-Stride, achieves a geometric mean IPC of 3.88 on the 135 public traces, as compared to 3.81 for the current leader of the Championship Value Prediction.