Lake Crest について調べてみた
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Intel の Deep Learning 用チップ Lake Crest、というか Nervana Engine についてざっと調べてみました。 （2016年12月の学部学生向け昼休みイベントの資料）
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Lake Crest について調べてみた
- 1. Intel Lake Crest Yutaka Yasuda, Kyoto Sangyo University, 2016/12/16
- 2. AI 2016.3 AlphaGO vs 2016.9 Google (AI) 2015 Google Photo “Google's AlphaGo AI Continues to Wallop Expert Human Go Player”, Popular Mechanics, 2016/3/10 http://www.popularmechanics.com/technology/a19863/googles-alphago-ai-wins-second-game-go/
- 3. Deep Learning 2014 ImageNet Google 20 2012 Google
- 4. ”Deep Visual-Semantic Alignments for Generating Image Descriptions”, Andrej Karpathy, Li Fei-Fei, Stanford University, CVPR 2015
- 7. Neural Network = Neuron https://en.wikipedia.org/wiki/Artificial_neural_network
- 8. “Introduction to multi gpu deep learning with DIGITS 2”, Mike Wang http://www.slideshare.net/papisdotio/introduction-to-multi-gpu-deep-learning-with-digits-2-mike-wang/6
- 9. “Introduction to multi gpu deep learning with DIGITS 2”, Mike Wang http://www.slideshare.net/papisdotio/introduction-to-multi-gpu-deep-learning-with-digits-2-mike-wang/6
- 10. “Introduction to multi gpu deep learning with DIGITS 2”, Mike Wang http://www.slideshare.net/papisdotio/introduction-to-multi-gpu-deep-learning-with-digits-2-mike-wang/6
- 12. Why nVIDIA?
- 13. Lake Crest
- 14. Intel Artificial Intelligence Day 2016/11/17 -12:30 PM PT San Francisco
- 18. ASIC
- 19. CPU ASIC GPU ASIC Wikipedia
- 20. “ ASIC ” Nervana Engine Web
- 21. 2.5D Blazingly fast data access via high-bandwith memory (HBM)
- 22. Processing Cluster x12 (3x4) ICL (Inter Chip Link) x12 8GB HBM2 x4
- 23. HBM?
- 24. An Introduction to HBM - High Bandwidth Memory - Stacked Memory and The Interposer http://www.guru3d.com/articles-pages/an-introduction-to-hbm-high-bandwidth-memory,2.html • HBM DRAM • • GPU Interposer • 2.5D
- 25. GDDR5 HBM2 32-bit Bus With 1024-bit Up-to 1750 MHz (7 Gbps) 2 Gbps Up-to 28 GB/s per chip 125GB/s (2Tb/s) per unit 1.5V 1.3V
- 26. LGA 2011: CPU 2011 Xeon E5 1600/2600 v4 Broadwell-EP 2000 1024 x4 → Wikipedia: LGA 2011
- 29. Tensor
- 31. https://www.tensorflow.org “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems”, Abdai, et. al, 2015, https://arxiv.org/abs/1603.04467v2
- 33. Nervana Engine ASIC Tensor HBM2 4 unit HBM 1024bit! 2.5D
- 35. 100Gbit/s *12
- 40. GPU SIMD GPU 32bit AI GPU nVIDIA CPU https://www.tensorflow.org/tutorials/mnist/beginners/
- 43. Binary Neural Network GPU 32bit BNN - Binarized Neural Network ( -1 / +1 ) Nervana Accelerating Neural Networks with Binary Arithmetic https://www.nervanasys.com/accelerating-neural-networks-binary-arithmetic/
- 44. “Accelerating Neural Networks with Binary Arithmetic” (blog post) These 32 bit floating point multiplications, however, are very expensive. In BNNs, floating point multiplications are supplanted with bitwise XNORs and left and right bit shifts. This is extremely attractive from a hardware perspective: binary operations can be implemented computationally efficiently at a low power cost. Nervana website (blog post) https://www.nervanasys.com/accelerating-neural-networks-binary-arithmetic/ 32bit BNN XNOR bit shift
- 45. Nervana Engine GPU SIMD BNN (ASIC) XNOR -1 0, +1 1 Tensor
- 47. GPU nVIDIA
- 48. Intel Xeon Phi http://www.4gamer.net/games/049/G004963/20161007061/
- 52. Deep Learning nVIDIA GPU Deep Learning Nervana Binalized HBM2 nVIDIA FP16 Intel AVX-512 SIMD Google TPU (Tensor Processing Unit) 8bit CPU!
- 53. Google
- 54. XNOR / CPU 100Gbps SIMD
- 55. 'You've got to find what you love,' Jobs says Steve Jobs, 2005, Stanford University https://www.youtube.com/watch?v=UF8uR6Z6KLc “Follow your heart”