Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2021

Abstract

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms. While the potential of deploying DNNs on resource-constrained platforms has been demonstrated by DNN compression techniques, the current practice suffers from two limitations: 1) merely stand-alone compression schemes are investigated even though each compression technique only suit for certain types of DNN layers; and 2) mostly compression techniques are optimized for DNNs’ inference accuracy, without explicitly considering other application-driven system performance (e.g., latency and energy cost) and the varying resource availability across platforms (e.g., storage and processing capability). To this end, we propose AdaDeep, a usage-driven, automated DNN compression framework for systematically exploring the desired trade-off between performance and resource constraints, from a holistic system level. Specifically, in a layer-wise manner, AdaDeep automatically selects the most suitable combination of compression techniques and the corresponding compression hyperparameters for a given DNN. Thorough evaluations on six datasets and across twelve devices demonstrate that AdaDeep can achieve up to 18.6×18.6×18.6× latency reduction, 9.8×9.8×9.8× energy-efficiency improvement, and 37.3×37.3×37.3× storage reduction in DNNs while incurring negligible accuracy loss. Furthermore, AdaDeep also uncovers multiple novel combinations of compression techniques.

Keywords

Optimization, Mobile Computing, Mobile Handsets, Mobile Applications, Energy Storage, Measurement, Computational Modeling

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Mobile Computing

Volume

20

Issue

12

First Page

3282

Last Page

3297

ISSN

1536-1233

Identifier

10.1109/TMC.2020.2999956

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.ieeecomputersociety.org/10.1109/TMC.2020.2999956

Share

COinS