  The rapidly growing demands for powerful AI algorithms in many application
domains have motivated massive investment in both high-quality deep neural
network (DNN) models and high-efficiency implementations. In this position
paper, we argue that a simultaneous DNN/implementation co-design methodology,
named Neural Architecture and Implementation Search (NAIS), deserves more
research attention to boost the development productivity and efficiency of both
DNN models and implementation optimization. We propose a stylized design
methodology that can drastically cut down the search cost while preserving the
quality of the end solution.As an illustration, we discuss this
DNN/implementation methodology in the context of both FPGAs and GPUs. We take
autonomous driving as a key use case as it is one of the most demanding areas
for high quality AI algorithms and accelerators. We discuss how such a
co-design methodology can impact the autonomous driving industry significantly.
We identify several research opportunities in this exciting domain.
