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When you already have a GPU in a system, adding tensor cores to it is much more efficient than adding a separate NPU which needs to replicate all the data transfer pipelines and storage buffers that the GPU already has. Besides, Nvidia's tensor cores are systolic.
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No, if that were the case, then Google would have made GPUs + NN cores vs TPUs.

There's far more microarchitectural complexity in GPUs that actually isn't efficient for NN structures.

"Systolic array" actually means something more specific than "repeated structures on a die."

Again, I'd suggest referencing the various HotChips presentations. It's a really interesting topic area. Or the original TPU v1 paper for the basics.


Why would Google need graphics functionality to train neural networks?



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