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Incremental Learning on Chip

G. B. Hacene, V. Gripon, N. Farrugia, M. Arzel and M. Jezequel, "Incremental Learning on Chip," in Proceedings of GlobalSip, 2017. To appear.

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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