Accelerating continual learning on edge FPGA

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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Bibliographic Details
Main Authors: Piyasena, Duvindu, Lam, Siew-Kei, Wu, Meiqing
Other Authors: College of Computing and Data Science
Format: Conference Paper
Language:English
Published: 2024
Subjects:
Computer and Information Science
FPGA
Deep learning
Online Access:https://hdl.handle.net/10356/178586

Internet

https://hdl.handle.net/10356/178586

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