Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core

Tomoya Itsubo, Mineto Tsukada, Hiroki Matsutani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Toward on-device learning on IoT devices, this paper implements an online sequential learning and unsupervised anomaly detection core and explores its design options, such as pipeline structure. They are evaluated in terms of performance and cost.

Original languageEnglish
Title of host publicationIEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728117485
DOIs
Publication statusPublished - 2019 May 23
Event22nd IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Yokohama, Japan
Duration: 2019 Apr 172019 Apr 19

Publication series

NameIEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings

Conference

Conference22nd IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019
CountryJapan
CityYokohama
Period19/4/1719/4/19

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Keywords

  • Machine learning and Pipeline structure)
  • On-device learning

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Itsubo, T., Tsukada, M., & Matsutani, H. (2019). Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core. In IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings [8721337] (IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CoolChips.2019.8721337