Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155784
Title: HSCoNAS : hardware-software co-design of efficient DNNs via neural architecture search
Authors: Luo, Xiangzhong
Liu, Di
Huai, Shuo
Liu, Weichen
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Luo, X., Liu, D., Huai, S. & Liu, W. (2021). HSCoNAS : hardware-software co-design of efficient DNNs via neural architecture search. 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). https://dx.doi.org/10.23919/DATE51398.2021.9473937
Project: MOE2019-T2-1-071 
MOE2019-T1-001-072 
M4082282 
M4082087 
Abstract: In this paper, we present a novel multi-objective hardware-aware neural architecture search (NAS) framework, namely HSCoNAS, to automate the design of deep neural networks (DNNs) with high accuracy but low latency upon target hardware. To accomplish this goal, we first propose an effective hardware performance modeling method to approximate the runtime latency of DNNs on target hardware, which will be integrated into HSCoNAS to avoid the tedious on-device measurements. Besides, we propose two novel techniques, \textit{i.e.}, dynamic channel scaling to maximize the accuracy under the specified latency and progressive space shrinking to refine the search space towards target hardware as well as alleviate the search overheads. These two techniques jointly work to allow HSCoNAS to perform fine-grained and efficient explorations. Finally, an evolutionary algorithm (EA) is incorporated to conduct the architecture search. Extensive experiments on ImageNet are conducted upon diverse target hardware, \textit{i.e.}, GPU, CPU, and edge device to demonstrate the superiority of HSCoNAS over recent state-of-the-art approaches.
URI: https://hdl.handle.net/10356/155784
DOI: 10.23919/DATE51398.2021.9473937
Rights: © 2021 EDAA, published by IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.23919/DATE51398.2021.9473937.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

Files in This Item:
File Description SizeFormat 
manuscript.pdf1.5 MBAdobe PDFView/Open

SCOPUSTM   
Citations 20

4
Updated on Jul 9, 2022

Page view(s)

46
Updated on Aug 16, 2022

Download(s)

8
Updated on Aug 16, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.