conference-paper

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

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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.

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Publication details

DOI
10.1109/coolchips.2019.8721337
OpenAlex
W2946995772
Document type
conference-paper
Language
EN
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