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Comment by stevesimmons | Hacker News Reader

Does anyone have experience of Reconfigure.io [1] and their toolchain that transpiles Go for execution on FPGAs [2]? I am curious how it feels from the developer perspective, how it works in practice, and what types of applications it is a good fit for.

[1] https://reconfigure.io

[2] http://docs.reconfigure.io/overview.html

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Unless you have optimized your code as much as possible to run on x86 or GPU and still find that you need a prohibitively expensive amount of processing power to accomplish the task, then and only then is an FPGA worth it.

It is literally a DIY CPU architecture. So, unless you know exactly what it is about your current CPU's architecture that is holding you back, you won't be able to benefit from an FPGA-based design.

I've waited 20 years for something like that, thanks for posting! FPGAs are great but I think the biggest problem with them is that they're an array of gates instead of logic units (like ALUs). So the learning curve to get to the point where you can place an array of cores and memories and have them all communicate is so high that most people never get that far.

So that's been a huge barrier to exploring the problem spaces that GPUs are currently used for like AI and physics simulations. Hopefully using languages like Go/Elixer/Erlang/MATLAB/Octave etc will alleviate that to some degree.

My gut feeling is that in 3-5 years we'll reach the limits of what SIMD can accomplish and we'll find it difficult to do the kinds of general-purpose MIMD computing needed to move beyond the basic building blocks of AI like neural nets and genetic algorithms. I stumbled onto these links a month and a half ago and I think something like this will make writing generalized/abstract highly parallelized code tractable again:

http://aggregate.org/MOG/

https://en.wikipedia.org/wiki/Flynn%27s_taxonomy

Original at: https://news.ycombinator.com/item?id=17419917

It's so far outside the model of FPGAs, that I wouldn't even consider putting in the effort. It's like how regular code doesn't run well on the GPU, but much worse.

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