Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core
Tomoya Itsubo, Mineto Tsukada, Hiroki Matsutani · 2019
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.