Efficient Hardware Implementation of Incremental Learning and Inference on Chip
Computer Vision and Pattern Recognition
2020-08-10 v1
Abstract
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.
Cite
@article{arxiv.1911.07847,
title = {Efficient Hardware Implementation of Incremental Learning and Inference on Chip},
author = {Ghouthi Boukli Hacene and Vincent Gripon and Nicolas Farrugia and Matthieu Arzel and Michel Jezequel},
journal= {arXiv preprint arXiv:1911.07847},
year = {2020}
}
Comments
In 2019 IEEE International NEWCAS Conference