conference-paper
Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core
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Paper overview
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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