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Lake Crest について調べてみた
The document discusses advancements in artificial intelligence, specifically focusing on deep learning techniques and hardware innovations like the Intel Nervana Engine. It references various studies, presentations, and technologies related to neural networks, GPUs, and the shift towards efficient binary neural networks (BNNs) to reduce computational costs. Additionally, it includes links to resources and articles that provide further insights into the field of AI and machine learning.
Lake Crest について調べてみた
- 1. Intel Lake Crest YutakaYasuda, Kyoto Sangyo University, 2016/12/16
- 2. AI 2016.3 AlphaGO vs 2016.9Google (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 ImageNetGoogle 20 2012 Google
- 4. ”Deep Visual-Semantic Alignments for GeneratingImage 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 multigpu 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 multigpu 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 multigpu deep learning with DIGITS 2”, Mike Wang http://www.slideshare.net/papisdotio/introduction-to-multi-gpu-deep-learning-with-digits-2-mike-wang/6
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- 14. Intel Artificial IntelligenceDay 2016/11/17 -12:30 PM PT San Francisco
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- 19. CPU ASIC GPUASIC Wikipedia
- 20. “ ASIC ” NervanaEngine Web
- 21. 2.5D Blazingly fast data accessvia high-bandwith memory (HBM)
- 22. Processing Cluster x12(3x4) ICL (Inter Chip Link) x12 8GB HBM2 x4
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- 24. An Introduction toHBM - 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 BusWith 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: CPU2011 Xeon E5 1600/2600 v4 Broadwell-EP 2000 1024 x4 → Wikipedia: LGA 2011
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- 31. https://www.tensorflow.org “TensorFlow: Large-Scale MachineLearning on Heterogeneous Distributed Systems”, Abdai, et. al, 2015, https://arxiv.org/abs/1603.04467v2
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- 33. Nervana Engine ASIC Tensor HBM2 4unit HBM 1024bit! 2.5D
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- 40. GPU SIMD GPU 32bit AIGPU nVIDIA CPU https://www.tensorflow.org/tutorials/mnist/beginners/
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- 43. Binary Neural Network GPU32bit 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 Networkswith 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
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- 48. Intel Xeon Phi http://www.4gamer.net/games/049/G004963/20161007061/
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- 52. Deep Learning nVIDIA GPU DeepLearning Nervana Binalized HBM2 nVIDIA FP16 Intel AVX-512 SIMD Google TPU (Tensor Processing Unit) 8bit CPU!
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- 54. XNOR / CPU 100Gbps SIMD
- 55. 'You've got tofind what you love,' Jobs says Steve Jobs, 2005, Stanford University https://www.youtube.com/watch?v=UF8uR6Z6KLc “Follow your heart”