To learn FPGA programming, I plan to code up a simple Neural Network in FPGA (since it's massively parallel; it's one of the few things where an FPGA implementation might have a chance of being faster than a CPU implementation).
Though I'm familiar with C programming (10+ years). I'm not so sure with FPGA development stuff. Can you provide a guided list of what I should do / learn / buy?
Thanks!
A FPGA can be used if the design requires complex logic and requires high processing ability and if the cost is comparable to the performance achieved. In case of a design that requires limited hardware, and is set to perform only some specific functions, then Microcontroller is preferred.
The backpropagation algorithm works by computing the gradient of the loss function with respect to each weight by the chain rule, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this is an example of dynamic ...
Neural networks can be simulated on a conventional computer but the main advantage of neural networks – parallel execution – is lost. Artificial neurons are not identical in operation to the biological ones.
Necroposting, but for others like me that come across this question there is an in-depth, though old, treatment of implementing neural networks using FPGAs
It's been three years since I posted this, but it is still being viewed so I thought I'd add another two papers from last year I recently found.
The first talks about FPGA Acceleration of Convolutional Neural Networks. Nallatech performed the work. It's more marketing that an academic paper, but still an interesting read, and might be a jumping off point for someone interesting in experimenting. I am not connected to Nallatech in any way.
The second paper came out of the University of Birmingham, UK, written by Yufeng Hao. It presents A General Neural Network Hardware Architecture on FPGA.
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