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That's not true. Previous gpus, like Pascal, have lower precision instructions for fp16 and int8. The tpu allows a 4x4 matrix multiply and accumulated in one c
by shaklee3 9y ago
That's not true. Previous gpus, like Pascal, have lower precision instructions for fp16 and int8. The tpu allows a 4x4 matrix multiply and accumulated in one clock cycle through special cores. They're physically different parts of the die.
- ktta 9y agoBut they're not a new paradigm as they are often stated in 'marketing talk'. Also, what are you referring to by 'tpu'? Google's TPU or nVidia's Tensor processing core? Because AFAIK, there aren't any details about TPU's processing pipeline other than that one paper about data center use. I realize they are physical parts of the die, which is why I said in my last sentence about how they take up unnecessary die space. Why? Because that die space will be better used for higher precision FP units which will be useful in training, which is more important than inference for most of the people in this thread.
- shaklee3 9y agoTpu is what Nvidia and Google both call their low precision matrix multiply hardware. Your assessment of how the hardware can better be used is just your assessment. The reason they did this is because they realized that a large share of their professional users want this, and they're willing to pay. They'll likely have other versions of the card where the tpus aren't there, so you'll have that option as well. It's not a new paradigm, but it's absolutely new to have this type of computation in a single cycle at these clock rates.