Abstract
Today's deep neural networks require substantial computation resources for their training, storage, and inference, which limits their effective use on resource-constrained devices. Many recent research activities explore different options for compressing and optimizing deep models for the Internet of Things (IoT). Our work so far has focused on two important aspects of deep neural network compression: class-dependent model compression and explainable compression. We shortly summarize our contributions and conclude with an outline of our future research directions.
Original language | English |
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Title of host publication | Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020 |
Pages | 385-386 |
Number of pages | 2 |
ISBN (Electronic) | 978-1-7281-5497-8 |
DOIs | |
Publication status | Published - 21 Apr 2020 |
Event | 19th ACM/IEEE International Conference on Information Processing in Sensor Networks: IPSN 2020 - Virtuell, Sydney, Australia Duration: 21 Apr 2020 → 24 Apr 2020 Conference number: 19 https://ipsn.acm.org/2020/ |
Publication series
Name | Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020 |
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Conference
Conference | 19th ACM/IEEE International Conference on Information Processing in Sensor Networks |
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Abbreviated title | IPSN 2020 |
Country/Territory | Australia |
City | Sydney |
Period | 21/04/20 → 24/04/20 |
Internet address |
Keywords
- deep neural networks
- Internet of Things
- model compression
ASJC Scopus subject areas
- Information Systems and Management
- Signal Processing
- Computer Networks and Communications