# Efficient Hardware Implementation of Incremental Learning and Inference on Chip

arXiv: https://arxiv.org/abs/1911.07847 · HTML: https://mlsanity.com/paper/1911.07847/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Efficient Majority Voting in Digital Hardware](https://mlsanity.com/paper/2108.03979/) (2021)

## Abstract (arXiv)

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.