  To cope with the ever-increasing computational demand of the DNN execution,
recent neural architecture search (NAS) algorithms consider hardware cost
metrics into account, such as GPU latency. To further pursue a fast, efficient
execution, DNN-specialized hardware accelerators are being designed for
multiple purposes, which far-exceeds the efficiency of the GPUs. However, those
hardware-related metrics have been proven to exhibit non-linear relationships
with the network architectures. Therefore it became a chicken-and-egg problem
to optimize the network against the accelerator, or to optimize the accelerator
against the network. In such circumstances, this work presents DANCE, a
differentiable approach towards the co-exploration of the hardware accelerator
and network architecture design. At the heart of DANCE is a differentiable
evaluator network. By modeling the hardware evaluation software with a neural
network, the relation between the accelerator architecture and the hardware
metrics becomes differentiable, allowing the search to be performed with
backpropagation. Compared to the naive existing approaches, our method performs
co-exploration in a significantly shorter time, while achieving superior
accuracy and hardware cost metrics.
