Accelerating continual learning on edge FPGA

Real-time edge AI systems operating in dynamic environments must learn quickly from streaming input samples without needing to undergo offline model training. We propose an FPGA accelerator for continual learning based on streaming linear discriminant analysis (SLDA), which is capable of class-incre...

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Détails bibliographiques
Auteurs principaux: Piyasena, Duvindu, Lam, Siew-Kei, Wu, Meiqing
Autres auteurs: College of Computing and Data Science
Format: Conference Paper
Langue:English
Publié: 2024
Sujets:
Accès en ligne:https://hdl.handle.net/10356/178586