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This is very useful, thanks!

And to answer your question on background and aims: I'm a machine learning engineer focusing on probabilistic graphical models (i.e. not deep learning) and would like to better understand how dedicated compute could (or could not) improve computational efficiency.

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For ML use case because there's so much data to process it is usually a better ROI to use GPUs from what I have seen. If you're pipeline is anything like Load a lot of data -> process data -> get output. If it is more like a streaming with less data then it might work out fine.

Also, hardware design is very different than software design. If you don't have a digital hardware engineer in your team it might be tricky to get what you want out of the board.

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