FPGAs don’t scale if they did all GPUs would’ve been replaced by FPGAs for graphics a long time ago.
You use an FPGA when spinning a custom ASIC doesn’t makes financial sense and generic processor such as a CPU or GPU is overkill.
Arguably the middle ground here are TPUs, just taking the most efficient parts of a “GPU” when it comes to these workloads but still relying on memory access in every step of the computation.
I thought it was because the number logic elements in a GPU is orders of magnitude higher than in a FPGA, rather than just processing speed. And GPU processing is inherently parallel so the GPU beats the FPGA just based on transistor count.
FPGAs would be less efficient than GPUs.
FPGAs don’t scale if they did all GPUs would’ve been replaced by FPGAs for graphics a long time ago.
You use an FPGA when spinning a custom ASIC doesn’t makes financial sense and generic processor such as a CPU or GPU is overkill.
Arguably the middle ground here are TPUs, just taking the most efficient parts of a “GPU” when it comes to these workloads but still relying on memory access in every step of the computation.
I thought it was because the number logic elements in a GPU is orders of magnitude higher than in a FPGA, rather than just processing speed. And GPU processing is inherently parallel so the GPU beats the FPGA just based on transistor count.