@inproceedings{fc3ce2792af9448c97edc2b2e4f61ee7,
title = "NAIS: Neural architecture and implementation search and its applications in autonomous driving",
abstract = "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.",
author = "Cong Hao and Hwu, \{Wen Mei\} and Junli Gu and Deming Chen and Yao Chen and Xinheng Liu and Atif Sarwari and Daryl Sew and Ashutosh Dhar and Bryan Wu and Dongdong Fu and Jinjun Xiong",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 38th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019 ; Conference date: 04-11-2019 Through 07-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ICCAD45719.2019.8942055",
language = "English",
series = "IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019 - Digest of Technical Papers",
address = "United States",
}