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About the AI algorithms, I'm not sure what you meant. Could you kindly explain?
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FPGAs are very good for processing streaming data, like that from an analog-to-digital converters or going to digital-to-analog converters. Hence FPGAs work nicely with signal processing applications.
What kinds of data do AI systems deal with? Do they need access to lots of parameters before making 'decisions'? Or are you designing little neurons that only talk to their local neighbors? These types of details will help you understand whether or not FPGAs can implement AI in the hardware fabric, or whether you will implement lots of simple processor cores, and run software on lots of cores within an FPGA. If it is the latter, then GPUs might be a more appropriate tool. So before investing time learning FPGAs, learn how to describe your AI system, so that you can determine the best mapping to hardware.
Cheers,
Dave
Honored Contributor
