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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 efficienthardware implementation of a recently introduced incremental learning procedurethat achieves state-of-the-art performance by combining transfer learning withmajority votes and quantization techniques. The proposed design is able toaccommodate for both new examples and new classes directly on the chip. Wedetail the hardware implementation of the method (implemented on FPGA target)and show it requires limited resources while providing a significantacceleration compared to using a CPU.

Efficient Hardware Implementation of Incremental Learning and Inference on Chip