Earlier quoted context omitted.
You don't seem bullish on the prospects of using Vitis [0] to deploy a machine learning model to a Xilinx FPGA? [0] https://www.xilinx.com/products/design-tools/vitis/vitis-pla...
Disclaimer: I work in this space (not at Xilinx), comments are strictly my own opinions and do not reflect any positions of my employer, etc. Broadly speaking, FPGA-based ML model accelerators are in an interesting space right now, where they aren't particularly compelling from a performance (or perf / Watt, perf / $, etc.) perspective. If you just need performance, then a GPU or ASIC-based accelerator will serve you…
you probably are aware but Xilinx themselves is attempting this with their versal aie boards which (in spirit) similar to GPUs, in that they group together a programmable fabric of programmable SIMD type compute cores.
https://www.xilinx.com/support/documentation/architecture-ma...
i have not played with one but i've been told (by a xilinx person, so grain of salt) the flow from high-level representation to that arch is more open