
A General Neural Network Hardware Architecture on FPGA [pdf] - Katydid
https://arxiv.org/ftp/arxiv/papers/1711/1711.05860.pdf
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banachtarski
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).

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marviel
This might interest you:
[http://neuromorphic.eecs.utk.edu/publications/2014-07-14-dyn...](http://neuromorphic.eecs.utk.edu/publications/2014-07-14-dynamic-
adaptive-neural-network-array/)

(Disclaimer --- I was a student working on this project)

~~~
ttul
Oh damn. Paywalled. It would be great if someone could throw their VHDL etc on
Github

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borramakot
Are there performance numbers for this, on e.g. resnet-50?

