HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling

December 06, 2022 · Entered Twilight · 🏛 Design, Automation and Test in Europe

Repo contents: .gitignore, LICENSE.md, README.md, environment.yml, search_algo, search_space, supernet

Authors Halima Bouzidi, Mohanad Odema, Hamza Ouarnoughi, Mohammad Abdullah Al Faruque, Smail Niar arXiv ID 2212.03354 Category cs.LG: Machine Learning Cross-listed cs.AR, cs.NE, cs.PF Citations 24 Venue Design, Automation and Test in Europe Repository https://github.com/HalimaBouzidi/HADAS ⭐ 11 Last Checked 2 months ago

Abstract

Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency. In many cases, the implementation of DyNNs can be sub-optimal due to its underlying backbone architecture being developed at the design stage independent of both: (i) the dynamic computing features, e.g. early exiting, and (ii) the resource efficiency features of the underlying hardware, e.g., dynamic voltage and frequency scaling (DVFS). Addressing this, we present HADAS, a novel Hardware-Aware Dynamic Neural Architecture Search framework that realizes DyNN architectures whose backbone, early exiting features, and DVFS settings have been jointly optimized to maximize performance and resource efficiency. Our experiments using the CIFAR-100 dataset and a diverse set of edge computing platforms have seen HADAS dynamic models achieve up to 57% energy efficiency gains compared to the conventional dynamic ones while maintaining the desired level of accuracy scores. Our code is available at https://github.com/HalimaBouzidi/HADAS