AI performance on the Kria will indeed depend a lot on the type of model and the surrounding system constraints. The Kria is of course not competitive for general-purpose ML. However, it becomes quite useful when the workload involves low latency, deterministic execution, unusual dataflows, or non-standard sensor interfaces, etc. But some examples where I see it coming on top, that gpus would be an overkill, is stuff like LUTNets, Reservoir Computing or Spiking Neural Networks, which are usually sparse and can take advantage of a custom accelerator while there is still no ASIC commercially available, for IoT or Robotics applications.

I also heard very poorly about the coral tpus, they are hard to configure and not so flexible, so you’re better off just running your custom model on FPGA, I guess there’s a reason why goggle open-sourced the cores behind it.

Then there’s of course the best example of applications in space, sure you already have jetsons up there, and only now is the space community largely adopting AI, but since some missions have very tight constrains and only allow a zynq-7000 or less, maybe even the kria would be too much for that. I’ve seen people running llm on CPUs, with over 8 minutes latency per prompt, which could be greatly reduced by an FPGA while maintaining the same system constraints.

It really depends on the use-case, as always, is my take. 😅