Support for ML model deployment

[marcelldls]

An established workflow for deploying ML models to the zync exists and an excellent video has been made about exactly this in the context of large experimental physics control systems by Josh at RadiaSoft https://www.youtube.com/watch?v=NWpcP_sFRm4. I met him at ICALEPCS this year and he explained that many of their filtering algorithms are being replaced with ML autoencoders in this manner. Further, im not sure if its interesting but in the same workflow it is actually possible to deploy any arbitrary algorithm on the Zync DPU written in Python PyTorch/numpy (it does not have to be something that is trained).

Is it an idea to add an ML model panda block?

[marcelldls]

A tutorial for deploying a demo ML model on an XU5 based on Xilinx Vitis AI can be found here: https://www.enclustra.com/en/products/design-in-kits/

[marcelldls]

A potentially interesting application for the PandA may be triggering based on recognition of a pattern in a signal