Paper Info
Title
Co-Exploring Neural Architecture and Network-on-Chip Design for Real-Time Artificial Intelligence
Abstract
Hardware-aware Neural Architecture Search (NAS), which automatically finds an architecture that works best on a given hardware design, has prevailed in response to the ever-growing demand for real-time Artificial Intelligence (AI). However, in many situations, the underlying hardware is not pre-determined. We argue that simply assuming an arbitrary yet fixed hardware design will lead to inferior solutions, and it is best to co-explore neural architecture space and hardware design space for the best pair of neural architecture and hardware design. To demonstrate this, we employ Network-on-Chip (NoC) as the infrastructure and propose a novel framework, namely NANDS, to co-explore NAS space and NoC Design Search (NDS) space with the objective to maximize accuracy and throughput. Since two metrics are tightly coupled, we develop a multi-phase manager to guide NANDS to gradually converge to solutions with the best accuracy-throughput tradeoff. On top of it, we propose techniques to detect and alleviate timing performance bottleneck, which allows better and more efficient exploration of NDS space. Experimental results on common datasets, CIFAR10, CIFAR-100 and STL-10, show that compared with state-of-the-art hardware-aware NAS, NANDS can achieve 42.99% higher throughput along with 1.58% accuracy improvement. There are cases where hardware-aware NAS cannot find any feasible solutions while NANDS can.
Year
DOI
Venue
2020
10.1109/ASP-DAC47756.2020.9045595
2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC)
Keywords
DocType
ISSN
network-on-chip design,real-time artificial intelligence,fixed hardware design,neural architecture space,hardware design space,NAS space,timing performance bottleneck,NDS space,NoC design search space,hardware-aware neural architecture search,real-time AI,NoC,NANDS framework,multiphase manager
Conference
2153-6961
ISBN
Citations 
PageRank 
978-1-7281-4124-4
3
0.43
References 
Authors
7
6
Name
Order
Citations
PageRank
Lei Yang19212.95
Weiwen Jiang29516.21
Weichen Liu341137.34
Edwin H.-M. Sha4131897.35
Yiyu Shi555383.22
Jingtong Hu696376.16