PhD Forum Abstract: Understanding Deep Model Compression for IoT Devices

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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 languageEnglish
Title of host publication2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
Pages385-386
Number of pages2
ISBN (Electronic)978-1-7281-5497-8
DOIs
Publication statusPublished - 21 Apr 2020
Event2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) - Virtuell, Australia
Duration: 21 Apr 202024 Apr 2020
Conference number: 19
https://ipsn.acm.org/2020/

Conference

Conference2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
Abbreviated titleIPSN 2020
CountryAustralia
CityVirtuell
Period21/04/2024/04/20
Internet address

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