  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.
