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Nvidia Titan V can do 110 TFLOPS, 12GB of 1.7 Gb/s Memory [1] and sells for 3,000$. TPU v2 does 180 TFLOPS, 64GB of 19.2Gb/s Memory [2]. That's a heck of a per
by cobookman 9y ago
Nvidia Titan V can do 110 TFLOPS, 12GB of 1.7 Gb/s Memory [1] and sells for 3,000$. TPU v2 does 180 TFLOPS, 64GB of 19.2Gb/s Memory [2].
That's a heck of a performance boost for a chip that's likely costing google way less than the nvidia flagship.
[1] http://www.tomshardware.com/news/nvidia-titan-v-110-teraflops,36085.html http://www.tomshardware.com/news/nvidia-titan-v-110-teraflop...
- shaklee3 9y agoIt's not clear to me how programmable the tpu is. I'm sure it's great at convolutions and matrix multiplies. Can it do anything else?
- EvgeniyZh 9y agoNeither do tensor cores
- shaklee3 9y agoThe tensor core is one part of the GPU. It has plenty of other capabilities.
- jlebar 9y agoWithout speaking to the capabilities of TPUs, note that most ML models today are mostly convolutions and matrix multiplies.
- PeterisP 9y agoWhat else should it be doing? It's an accelerator to run Tensorflow graphs, and TF graphs essentially are converted to matrix operations and convolutions.
- nl 9y agoIt'd be really interesting to know the per-unit math on that. Designing and taping out a new ASIC isn't cheap. Presumably Google needs to use a fairly recent process (22nm or better?), which means GlobalFoundaries/TSMC or Samsung (do any of the Chinese native fabs have 22nm yet?). I wonder who us building them? So many questions...
- argonaut 9y agoThe TFLOPS numbers are not directly comparable. The TPUs use reduced precision in some areas, whereas I am guessing the Titan V numbers are based on single precision operations.