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[D] Difference between CUDA and Tensor Cores (alien.top)

submitted 10 months ago by 3DHydroPrints@alien.top to c/machinelearning@academy.garden

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CUDA cores (or shader cores in general) have long been used to compute graphics. A very often used operation in computer graphics are matrix multiplications, just like in deep learning. Back in the days (AlexNet) NNs were computed using shader cores, but now have completely moved to be computed on Tensor cores. My question are:

Why have these workloads been seperated? (Yes obviously the tensor cores are more specialized and leave out a bunch of unnecessary operations, but how and why not integrate it into the CUDA cores to boost MM operations for computer graphics?)
Why isn't the workload offloaded to the other cores when the mathematical operations are the same
What makes tensor cores so much more efficient and faster?

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[–] wen_mars@alien.top 1 points 10 months ago

And recently tensor cores have started appearing with 8 bit float/int as well, which gives them a huge advantage in inference throughput. The memory bandwidth limitation can be mitigated by increasing the batch size.