This paper was extremely devoid of content beyond a basic summary of what neural nets are, what an FPGA is, some basics about both (forward propagation, back propagation, usage of HDL modules, usage of LUTs), and a statement that the author created a thing.

As a machine learning guy and total closet FPGA geek, this was sort of a disappointment. I would have liked to see topics addressed like floating point precision, actual benchmarks, how the FPGA can pipeline things better than best known CPU or GPU algorithms due to a lack of pipeline stalls, issues with I/O of training data and predictions, and probably a discussion on LSTM gates or GRUs (which I think the FPGA is particularly suitable for).