OS-ELM-FPGA: An FPGA-based online sequential unsupervised anomaly detector

Mineto Tsukada, Masaaki Kondo, Hiroki Matsutani

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

3 Citations (Scopus)

Abstract

Autoencoder, a neural-network based dimensionality reduction algorithm has demonstrated its effectiveness in anomaly detection. It can detect whether an input sample is normal or abnormal by just training only with normal data. In general, Autoencoder is built on backpropagation-based neural networks (BP-NNs). When BP-NNs are implemented in edge devices, they are typically specialized only for prediction with weight matrices precomputed offline due to the high computational cost. However, such devices cannot be immediately adapted to time-series trend changes of input data. In this paper, we propose an FPGA-based unsupervised anomaly detector, called OS-ELM-FPGA, that combines Autoencoder and an online sequential learning algorithm OS-ELM. Based on our theoretical analysis of the algorithm, the proposed OS-ELM-FPGA completely eliminates matrix pseudoinversions while improving the learning throughput. Simulation results using open-source datasets show that OS-ELM-FPGA achieves favorable anomaly detection accuracy compared to CPU and GPU implementations of BP-NNs. Learning throughput of OS-ELM-FPGA is 3.47x to 27.99x and 5.22x to 78.06x higher than those of CPU and GPU implementations of OS-ELM. It is also 3.62x to 36.15x and 1.53x to 43.44x higher than those of CPU and GPU implementations of BP-NNs.

Original languageEnglish
Title of host publicationEuro-Par 2018
Subtitle of host publicationParallel Processing Workshops - Euro-Par 2018 International Workshops, Revised Selected Papers
EditorsGabriele Mencagli, Dora B. Heras
PublisherSpringer Verlag
Pages518-529
Number of pages12
ISBN (Print)9783030105488
DOIs
Publication statusPublished - 2019 Jan 1
Event24th International Conference on Parallel and Distributed Computing, Euro-Par 2018 - Turin, Italy
Duration: 2018 Aug 272018 Aug 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11339 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Parallel and Distributed Computing, Euro-Par 2018
CountryItaly
CityTurin
Period18/8/2718/8/28

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Tsukada, M., Kondo, M., & Matsutani, H. (2019). OS-ELM-FPGA: An FPGA-based online sequential unsupervised anomaly detector. In G. Mencagli, & D. B. Heras (Eds.), Euro-Par 2018: Parallel Processing Workshops - Euro-Par 2018 International Workshops, Revised Selected Papers (pp. 518-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11339 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-10549-5_41