Not yet — I'm currently testing on a Zynq-7000 platform (embedded-class FPGA), mainly because it has an ARM CPU tightly integrated (and it's rather cheap).
I use the ARM side to handle IO and orchestration, which let me focus the FPGA fabric purely on the Python execution core, without having to build all the peripherals from scratch at this stage.
To run PyXL on a server-class FPGA (like Azure instances), some adaptations would be needed — the system would need to repurpose the host CPU to act as the orchestrator, handling memory, IO, etc.
The question is: what's the actual use case of running on a server? Besides testing max frequency -- for which I could just run Vivado on a different target (would need license for it though)
For now, I'm focusing on validating the core architecture, not just chasing raw clock speeds.
To run PyXL on a server-class FPGA (like Azure instances), some adaptations would be needed — the system would need to repurpose the host CPU to act as the orchestrator, handling memory, IO, etc.
The question is: what's the actual use case of running on a server? Besides testing max frequency -- for which I could just run Vivado on a different target (would need license for it though)
For now, I'm focusing on validating the core architecture, not just chasing raw clock speeds.