June 11, 2024, 4:47 a.m. | Binglei Lou, David Boland, Philip H. W. Leong

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.05999v1 Announce Type: cross
Abstract: Machine learning ensembles combine multiple base models to produce a more accurate output. They can be applied to a range of machine learning problems, including anomaly detection. In this paper, we investigate how to maximize the composability and scalability of an FPGA-based streaming ensemble anomaly detector (fSEAD). To achieve this, we propose a flexible computing architecture consisting of multiple partially reconfigurable regions, pblocks, which each implement anomaly detectors. Our proof-of-concept design supports three state-of-the-art anomaly …

abstract anomaly anomaly detection arxiv composability cs.ai cs.ar cs.lg detection ensemble fpga library machine machine learning multiple output paper scalability streaming type

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