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

Tomoya Itsubo; Mineto Tsukada; Hiroki Matsutani

doi:10.1109/CoolChips.2019.8721337

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

Tomoya Itsubo, Mineto Tsukada, Hiroki Matsutani

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728117485
DOIs	https://doi.org/10.1109/CoolChips.2019.8721337
Publication status	Published - 2019 May 23
Event	22nd IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Yokohama, Japan Duration: 2019 Apr 17 → 2019 Apr 19

Publication series

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

Conference

Conference	22nd IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019
Country/Territory	Japan
City	Yokohama
Period	19/4/17 → 19/4/19

Keywords

Machine learning and Pipeline structure)
On-device learning

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/CoolChips.2019.8721337

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

Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core. / Itsubo, Tomoya; Tsukada, Mineto; Matsutani, Hiroki.

IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8721337 (IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 22nd IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019, Yokohama, Japan, 19/4/17. https://doi.org/10.1109/CoolChips.2019.8721337

Itsubo T, Tsukada M, Matsutani H. 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. Institute of Electrical and Electronics Engineers Inc. 2019. 8721337. (IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings). https://doi.org/10.1109/CoolChips.2019.8721337

Itsubo, Tomoya ; Tsukada, Mineto ; Matsutani, Hiroki. / Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core. IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings).

@inproceedings{125586c8088c4fa39917c200f845d17d,

title = "Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core",

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.",

keywords = "Machine learning and Pipeline structure), On-device learning",

author = "Tomoya Itsubo and Mineto Tsukada and Hiroki Matsutani",

note = "Funding Information: Acknowlegements This work was supported by VDEC, the University of Tokyo in collaboration with Synopsys. References [1] Mineto Tsukada, Masaaki Kondo, and Hiroki Matsutani. OS-ELM-FPGA: An FPGA-Based Online Sequential Unsuper-vised Anomaly Detector. In Proceedings of the International European Conference on Parallel and Distributed Computing (Euro-Par{\textquoteright}18) Workshops, pages 518–529, Aug 2018. [2] N. Y. Liang, G. B. Huang, P. Saratchandran, and N. Sundararajan. A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks. IEEE Transactions on Neural Networks, 17(6):1411–1423, Nov 2006. [3] G. Hinton and R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786):504– 507, Jul 2006. Publisher Copyright: {\textcopyright} 2019 IEEE.; 22nd IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 ; Conference date: 17-04-2019 Through 19-04-2019",

year = "2019",

month = may,

day = "23",

doi = "10.1109/CoolChips.2019.8721337",

language = "English",

series = "IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings",

}

TY - GEN

T1 - Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core

AU - Itsubo, Tomoya

AU - Tsukada, Mineto

AU - Matsutani, Hiroki

N1 - Funding Information: Acknowlegements This work was supported by VDEC, the University of Tokyo in collaboration with Synopsys. References [1] Mineto Tsukada, Masaaki Kondo, and Hiroki Matsutani. OS-ELM-FPGA: An FPGA-Based Online Sequential Unsuper-vised Anomaly Detector. In Proceedings of the International European Conference on Parallel and Distributed Computing (Euro-Par’18) Workshops, pages 518–529, Aug 2018. [2] N. Y. Liang, G. B. Huang, P. Saratchandran, and N. Sundararajan. A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks. IEEE Transactions on Neural Networks, 17(6):1411–1423, Nov 2006. [3] G. Hinton and R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786):504– 507, Jul 2006. Publisher Copyright: © 2019 IEEE.

PY - 2019/5/23

Y1 - 2019/5/23

N2 - 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.

AB - 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.

KW - Machine learning and Pipeline structure)

KW - On-device learning

UR - http://www.scopus.com/inward/record.url?scp=85067121255&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067121255&partnerID=8YFLogxK

U2 - 10.1109/CoolChips.2019.8721337

DO - 10.1109/CoolChips.2019.8721337

M3 - Conference contribution

AN - SCOPUS:85067121255

T3 - IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019

Y2 - 17 April 2019 through 19 April 2019

ER -

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this