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.
@article{arxiv.1911.07446,
title = {NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving},
author = {Cong Hao and Yao Chen and Xinheng Liu and Atif Sarwari and Daryl Sew and Ashutosh Dhar and Bryan Wu and Dongdong Fu and Jinjun Xiong and Wen-mei Hwu and Junli Gu and Deming Chen},
journal= {arXiv preprint arXiv:1911.07446},
year = {2019}
}