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.
Originalsprache | englisch |
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Titel | Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020 |
Seiten | 385-386 |
Seitenumfang | 2 |
ISBN (elektronisch) | 978-1-7281-5497-8 |
DOIs | |
Publikationsstatus | Veröffentlicht - 21 Apr. 2020 |
Veranstaltung | 19th ACM/IEEE International Conference on Information Processing in Sensor Networks: IPSN 2020 - Virtuell, Sydney, Australien Dauer: 21 Apr. 2020 → 24 Apr. 2020 Konferenznummer: 19 https://ipsn.acm.org/2020/ |
Publikationsreihe
Name | Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020 |
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Konferenz
Konferenz | 19th ACM/IEEE International Conference on Information Processing in Sensor Networks |
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Kurztitel | IPSN 2020 |
Land/Gebiet | Australien |
Ort | Sydney |
Zeitraum | 21/04/20 → 24/04/20 |
Internetadresse |
ASJC Scopus subject areas
- Informationssysteme und -management
- Signalverarbeitung
- Computernetzwerke und -kommunikation