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How does this compare with Google's proprietary neural network optimized chip? That seems like a place that Intel could kick ass in and sell tons to Amazon and

How does this compare with Google's proprietary neural network optimized chip? That seems like a place that Intel could kick ass in and sell tons to Amazon and Microsoft's cloud services for NN acceleration, especially if they supported the main NN libraries. And if Intel isn't pursuing the opportunity to compete for deep learning NN against Google's chips, why aren't they? It seems like a well defined opportunity, more so than whatever these chips are.

dnautics 9y ago
I don't know for sure, but google's chips are advanced matrix multiplying engines. the "neuromorphic" chips are (in a very stupid-fied explanation) kind of like imagine a configurable fpgas with more-than-one-bit lines connecting adjacent tiles.
- copperx 9y ago
  > google's chips are advanced matrix multiplying engines Excuse my ignorance, but aren't GPUs exactly that?
  - rileyphone 9y ago
    From what I can tell, Google's TPUs are boiling GPUs down to the essence of what they do in TensorFlow, namely a "higher volume of reduced precision computation" [1]. [1] https://en.wikipedia.org/wiki/Tensor_processing_unit https://en.wikipedia.org/wiki/Tensor_processing_unit
  - hedgehog 9y ago
    GPUs are generally much more flexible and can run something resembling a normal C program. From the little public information available chips like Google TPU or NVIDIA's Tensor Core (a functional unit on the Volta architecture) are essentially hard-wired to do exactly low precision matrix multiplication. Less flexible but more efficient when you know that's what your workload is. https://devblogs.nvidia.com/parallelforall/inside-volta/ https://devblogs.nvidia.com/parallelforall/inside-volta/ https://www.nextplatform.com/2017/04/05/first-depth-look-googles-tpu-architecture/ https://www.nextplatform.com/2017/04/05/first-depth-look-goo...
  - dnautics 9y ago
    no, GPUs are vector math engines. To be fair, you can assemble a matrix multiply from vector math, but it is not quite the same. In spirit, GPUs are closer to matrix engines than neuromorphic chips.

dnautics 9y ago

I don't know for sure, but google's chips are advanced matrix multiplying engines. the "neuromorphic" chips are (in a very stupid-fied explanation) kind of like imagine a configurable fpgas with more-than-one-bit lines connecting adjacent tiles.

copperx 9y ago
> google's chips are advanced matrix multiplying engines Excuse my ignorance, but aren't GPUs exactly that?
- rileyphone 9y ago
  From what I can tell, Google's TPUs are boiling GPUs down to the essence of what they do in TensorFlow, namely a "higher volume of reduced precision computation" [1]. [1] https://en.wikipedia.org/wiki/Tensor_processing_unit https://en.wikipedia.org/wiki/Tensor_processing_unit
- hedgehog 9y ago
  GPUs are generally much more flexible and can run something resembling a normal C program. From the little public information available chips like Google TPU or NVIDIA's Tensor Core (a functional unit on the Volta architecture) are essentially hard-wired to do exactly low precision matrix multiplication. Less flexible but more efficient when you know that's what your workload is. https://devblogs.nvidia.com/parallelforall/inside-volta/ https://devblogs.nvidia.com/parallelforall/inside-volta/ https://www.nextplatform.com/2017/04/05/first-depth-look-googles-tpu-architecture/ https://www.nextplatform.com/2017/04/05/first-depth-look-goo...
- dnautics 9y ago
  no, GPUs are vector math engines. To be fair, you can assemble a matrix multiply from vector math, but it is not quite the same. In spirit, GPUs are closer to matrix engines than neuromorphic chips.