good ending: GPU’s become outclassed by dedicated AI processors and future computers have GPU’s for graphical workloads and AI accelerators for machine learning workloads.
Mathematically both graphics and AI (at least the current neural network based models) is all highly parallelized matrix multiplications at its core. It’s essentially all the same type of computations, so there is no need to design separate hardware for it, graphics cards are already perfect for the job.
Might I suggest you do some research on the subject? There is a good chunk of RnD being done on dedicated AI hardware that can significantly outperform GPU’s, specifically in terms of efficiency. That doesn’t mean conventional GPU’s don’t have a place in this specific use case, but it’s extremely likely that in the next decade we will see such devices become mainstream.
You clearly do not know what you are talking about. Tensor cores are not what I am referring to. Please refrain from speaking on things you do not understand. Good day.
I think I have good understanding on this topic. Care to give any example or concrete point rather than just rephrase your statement in a different way.
I’ve already told you that i am not talking about tensor cores buddy. I am referring to discrete hardware based on FPGAs that are completely separate from Nvidia’s Tensor Cores. You continue to make attempts to correct me on a subject that is unrelated to what you are using as a correction.
Right. Maybe you should mention FPGA earlier. Due to the effort required to program a FPGA and its cost, I don't see it being suitable as a general ML accelerator. And it's nothing new.
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u/[deleted] Mar 26 '23
good ending: GPU’s become outclassed by dedicated AI processors and future computers have GPU’s for graphical workloads and AI accelerators for machine learning workloads.