  Hardware-Software Co-Design is a highly successful strategy for improving
performance of domain-specific computing systems. We argue for the application
of the same methodology to deep learning; specifically, we propose to extend
neural architecture search with information about the hardware to ensure that
the model designs produced are highly efficient in addition to the typical
criteria around accuracy. Using the task of keyword spotting in audio on edge
computing devices, we demonstrate that our approach results in neural
architecture that is not only highly accurate, but also efficiently mapped to
the computing platform which will perform the inference. Using our modified
neural architecture search, we demonstrate $0.88\%$ increase in TOP-1 accuracy
with $1.85\times$ reduction in latency for keyword spotting in audio on an
embedded SoC, and $1.59\times$ on a high-end GPU.
