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Incremental Learning on Chip

G. B. Hacene, V. Gripon, N. Farrugia, M. Arzel et M. Jezequel, "Incremental Learning on Chip," dans Proceedings of GlobalSip, 2017. À paraître.

Learning on chip (LOC) is a challenging problem in which an embedded system learns a model and uses it to process and classify unknown data, while adapting to new observations or classes. It may require intensive computational power to adapt to new data, leading to a complex hardware implementation. We address this issue by introducing an incremental learning method based on the combination of a pre-trained Convolutional Neural Network (CNN) and majority votes, using Product Quantizing (PQ) as a bridge between them. We detail a hardware implementation of the proposed method (validated on a FPGA target) using limited hardware resources while providing substantial processing acceleration compared to a CPU counterpart.

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Bibtex
@inproceedings{HacGriFarArzJez2017,
  author = {Ghouthi Boukli Hacene and Vincent Gripon
and Nicolas Farrugia and Matthieu Arzel and Michel
Jezequel},
  title = {Incremental Learning on Chip},
  booktitle = {Proceedings of GlobalSip},
  year = {2017},
  note = {To appear},
}




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