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...
| Auteurs principaux: | , , |
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| Autres auteurs: | |
| Format: | Conference Paper |
| Langue: | English |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://hdl.handle.net/10356/178586 |