Intel to buy deep-learning startup Nervana Systems for at least $350M
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Re: Intel to buy deep-learning startup Nervana Systems for at least $350M
#2Summary:
- 28nm
- looks similar to P100 (interposer with HBM)
- 55 teraops/s performance
- custom number format (variable length fixed point?)
- simplified memory architecture (no cache?)
- no info about power consumption
Re: Intel to buy deep-learning startup Nervana Systems for at least $350M
#3Re: Intel to buy deep-learning startup Nervana Systems for at least $350M
#4Also lets Nervana scale and potentially get their Neon deep learning framework out there in the face of bigger players (a la TensorFlow).
All in all it's good to see the competition in this space.
Re: Intel to buy deep-learning startup Nervana Systems for at least $350M
#5@OP: that's Nervana with 'e' ;)
Re: Intel to buy deep-learning startup Nervana Systems for at least $350M
#6Likely a good move for Intel to get in on one of the faster growing areas of computing. Let's the compete with the GPU folks and offer something to the cloud players eventually (a la Tensor Processing Unit). Nice to see the competition. Also lets Nervana scale and potentially get their Neon deep learning framework out there in the face of bigger players (a la TensorFlow). All in all it's good to see the competition i…
Re: Intel to buy deep-learning startup Nervana Systems for at least $350M
#7As good as this could be, it would be even better if we also get an open-source software stack that can compete with Nvidia's proprietary CUDA stack, which currently dominates everywhere (except maybe in Google's data centers).
Re: Intel to buy deep-learning startup Nervana Systems for at least $350M
#8Congratulations!
Re: Intel to buy deep-learning startup Nervana Systems for at least $350M
#9Re: Intel to buy deep-learning startup Nervana Systems for at least $350M
#10Here are some details about their upcoming chip: http://www.nextplatform.com/2016/08/08/deep-learning-chip-up... Summary: - 28nm - looks similar to P100 (interposer with HBM) - 55 teraops/s performance - custom number format (variable length fixed point?) - simplified memory architecture (no cache?) - no info about power consumption