That's why I wanted to start this conversation, I think it's germane to GPU efforts and people may not be aware. I would be more excited about a project like this spreading FPGA tech, and more effort being spent there. My company is doing it and we want a bigger open-source community with us. (We're not in a position to release code yet, but we will.)
Effort is energy. I'm hoping mentioning these technologies will give people pursuing projects like this some options on where to spend their efforts. There's a finite pool of people who are working in this space and I hope it grows.
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?
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 said, working with the GPU requires a very specialized set of high level constructions as well; I imagine that most workloads could have significant parts of their computational path abstracted out to pretty common easy to port fpga processes.
If you ever launch a beta...let me know!
I think it's not as much a matter of making an FPGA that would be easier to program as it is a matter of coming up with an order-of-magnitude of improvement in programming techniques. What you're demanding is not far removed from saying that better programming models require improved transistors.