Efficient Hardware Implementation of Incremental Learning and Inference on Chip

Autor:	Hacene, Ghouthi Boukli, Gripon, Vincent, Farrugia, Nicolas, Arzel, Matthieu, Jezequel, Michel
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: In 2019 IEEE International NEWCAS Conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.07847 Zobrazit plný text záznamu View this record from Arxiv