I havent found a lot of information on current happenings relating to implementation of neural networks on FPGA.
What I am really looking for right now is a real world application which would actually exploit the reconfigurability aspect of the FPGA.
One of those many fields holding interest were Density enhancement using FPGA. This is much based on work published by Bringham Young university, but since the hardware available would allow us to work a lot more in depth, and the reconfiguration time has gone down a lot, I am looking to see if the density of neurons can further be enhanced by piplelining and cashing the modularity of neural networks.
I am new to this and so am not sure if it is even worth trying, as to concerns about time ( obviously would want to get a speed up over the software)
Please help me with some more ideas for applications, and please let me know if my idea holds any weight.
Regards, Pradeep