An Area-Efficient Implementation of Recurrent Neural Network Core for Unsupervised Anomaly Detection

Takuya Sakuma, Hiroki Matsutani

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

Abstract

Toward on-device anomaly detection for time-series data, in this paper, we analyze Echo State Network (ESN), which is a simple form of Recurrent Neural Networks (RNNs), and propose its area-efficient implementation. It is evaluated in terms of the anomaly detection capability and area. (Keywords: On-device learning, Machine learning, and Anomaly detection)

Original languageEnglish
Title of host publicationIEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728163475
DOIs
Publication statusPublished - 2020 Apr
Event23rd IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2020 - Kokubunji, Japan
Duration: 2020 Apr 152020 Apr 17

Publication series

NameIEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2020 - Proceedings

Conference

Conference23rd IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2020
CountryJapan
CityKokubunji
Period20/4/1520/4/17

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Hardware and Architecture
  • Software
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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    Sakuma, T., & Matsutani, H. (2020). An Area-Efficient Implementation of Recurrent Neural Network Core for Unsupervised Anomaly Detection. In IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2020 - Proceedings [9097631] (IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/COOLCHIPS49199.2020.9097631