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Have you tested it on any faster FPGAs? I think Azure has instances with xilinx/AMD accelerators paired. >Standard_NP10s instance, 1x AMD Alveo U250 FPGA (64GB
by tgtweak 1y ago
Have you tested it on any faster FPGAs? I think Azure has instances with xilinx/AMD accelerators paired.
>Standard_NP10s instance, 1x AMD Alveo U250 FPGA (64GB)
Would be curious to see how this benchmarks on a faster FGPA since I imagine clock frequency is the latency dictator - while memory and tile can determine how many instances can run in parallel.
- hwpythonner 1y agoNot 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.
- zoobab 1y agoYou can get cheap Zynq boards on Aliexpress, like old mining boards. I have a Paralella board here with a Zynq.