  While neural network hardware accelerators provide a substantial amount of
raw compute throughput, the models deployed on them must be co-designed for the
underlying hardware architecture to obtain the optimal system performance. We
present a class of computer vision models designed using hardware-aware neural
architecture search and customized to run on the Edge TPU, Google's neural
network hardware accelerator for low-power, edge devices. For the Edge TPU in
Coral devices, these models enable real-time image classification performance
while achieving accuracy typically seen only with larger, compute-heavy models
running in data centers. On Pixel 4's Edge TPU, these models improve the
accuracy-latency tradeoff over existing SoTA mobile models.
