FPGAs don’t scale if they did all GPUs would’ve been replaced by FPGAs for graphics a long time ago.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.
dogma1138|7 days ago
FPGAs don’t scale if they did all GPUs would’ve been replaced by FPGAs for graphics a long time ago.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.
jgalt212|7 days ago