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Communication Dans Un Congrès Année : 2020

Efficient Hardware Implementation of Incremental Learning and Inference on Chip

Résumé

In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end, we propose an efficient hardware implementation of a recently introduced incremental learning procedure that achieves state-of-the-art performance by combining transfer learning with majority votes and quantization techniques. The proposed design is able to accommodate for both new examples and new classes directly on the chip. We detail the hardware implementation of the method (implemented on FPGA target) and show it requires limited resources while providing a significant acceleration compared to using a CPU.

Dates et versions

hal-02487476 , version 1 (21-02-2020)

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Citer

Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia, Matthieu Arzel, Michel Jezequel. Efficient Hardware Implementation of Incremental Learning and Inference on Chip. NEWCAS 2019 : 17th IEEE International New Circuits and Systems Conference, Jun 2019, Munich, Germany. ⟨10.1109/NEWCAS44328.2019.8961310⟩. ⟨hal-02487476⟩
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