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
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 -