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Comment by modeless | Hacker News Reader

Agreed that accessible FPGA SoCs are super exciting and I want to learn more about them. In the past when I've looked at these I've always been disappointed in the raw FLOPS available vs. GPUs, though it's difficult to compare directly, and I'm totally unfamiliar with the FPGA world. If I buy e.g. a ZYBO, make something cool, and want to scale it up to beat e.g. an NVIDIA GTX Titan in raw performance (4.5 TFLOPS), what are my options?

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I guess it really depends on the application space you're interested in. We are building a cloud-FPGA platform but the barrier is just as you mention- the user being able do the programming. The idea is more people could scale their projects using the platform, but holy crap do we have a ways to go. We're going to lease out algorithmic services, basically, since getting a customer's design to work on our gear is more than difficult, there's way too much hardware variety. Portability is practically nonexistent, and that's what we're specifically trying to address.

Our approach for scaling is a monster FPGA board we're developing. It has a Zynq on it. It will be extremely inexpensive in terms of value next to, say, a highly-capable production server.

What kind of projects would you be working on if you bought a Zynq board?

Cloud FPGAs sound awesome. I'm interested in computer vision and machine learning, specifically "deep learning" neural nets. The current state of the art in neural nets is GPU implementations, and I think it's the right time to start thinking about FPGAs and eventually ASICs.

For deep learning, high performance in a single machine is important, as it's very difficult to distribute learning across multiple machines in an efficient way. A more powerful machine can train a bigger network in the same amount of time, and a bigger network can learn more complex things, with no known upper bound.

That's a great usage of FPGAs, and part of the platform will enable you to choose what algorithms you want to boot on your cluster slice. (In the FPGA space these pre-built algorithm circuits are called "IP blocks".) we will definitely be offering machine learning algorithms, with an eye towards minimizing data storage as much as possible. That may sound counter-intuitive, but there's a method to the madness (hopefully!)

I can imagine an fpga-cloud with an attached marketplace: you need performance for a certain algorithm, hardware engineers will compete and customize to your needs. I can think of quite a few engineers who'd love to participate.

That's the idea. I've just put my email address in my profile, please contact me about this if you like.

Your email is not visible, can you add it?

Actually I just reread and saw you used the word "compete", I had read it as "complete". Now that's a cool idea you have there.

Will this be similar to Parallella at all?

Parallella is an incredible project that I'm rooting for passionately. I am really looking forward to contributing to it, but we're more commercially oriented. Parallella is an Arduino/RP kind of project, and I have a feeling I will learn things from it. I, personally, still have a lot to learn in this space. Actually it feels kind of like a boundless area for learning. I really believe this is the next phase of computation, and that programming for non-Turing machines is the step that will prepare us for spintronics and quantum. I can't imagine Windows for Spintronics, I just can't. Our current software platforms, especially OSes, are going to have to change, and we want to start now. But the path is unclear- the full potential of reconfigurable computing has not yet been realized. That's why I find it exciting.

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