"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Presentation from Baidu
For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-baidu For more information about embedded vision, please visit: http://www.embedded-vision.com Dr. Ren Wu, former distinguished scientist at Baidu's Institute of Deep Learning (IDL), presents the keynote talk, "Enabling Ubiquitous Visual Intelligence Through Deep Learning," at the May 2015 Embedded Vision Summit. Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans. Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints? In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu. Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015) Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”
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"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Presentation from Baidu
- 1. Enabling Ubiquitous Visual Intelligence Through Deep Learning Dr. Ren Wu Distinguished Scientist, Baidu wuren@baidu.com @韧在百度
- 2. Dr. Ren Wu • Distinguished Scientist, Baidu • HSA Chief Software Architect, AMD • PI, HP Labs CUDA Research Center • World Computer Xiangqi Champion • AI expert • Heterogeneous Computing expert • Computational scientist
- 3. Eight Years Ago - 05/11/1997
- 4. Deep Blue A classic example of application-specific system design comprised of an IBM supercomputer with 480 custom-madeVLSI chess chips, running massively parallel search algorithm with highly optimized implementation.
- 5. Computer Chess and Moore’s Law
- 6. Deep Learning Works “We deepened our investment in advanced technologies like Deep Learning, which is already yielding near term enhancements in customer ROI and is expected to drive transformational change over the longer term.” – Robin Li, Baidu CEO
- 7. Amount of data Performance Deep learning Old algorithms Deep Learning
- 8. Deep Learning vs. Human Brain pixels edges object parts (combination of edges) object models Deep Architecture in the Brain Retina Area V1 Area V2 Area V4 pixels Edge detectors Primitive shape detectors Higher level visual abstractions Slide credit: Andrew Ng Voice Text Image User
- 9. Deep Convolutional Neural Networks * Efficient mapping of the training of Convolutional Neural Networks to a CUDA-based cluster courtesy of Jonatan Ward, Sergey Andreev, Francisco Heredia, Bogdan Lazar, Zlatka Manevska
- 10. Big Data • >2000PBStorage • 10-100PB/dayProcessing • 100b-1000bWebpages • 100b-1000bIndex • 1b-10b/dayUpdate • 100TB~1PB/dayLog
- 11. Heterogeneous Computing 1993 world #1 Think Machine CM5/1024 131 GFlops 2013 Samsung Note 3 smartphone (Qualcomm SnapDragon 800) 129 Gflops 2000 world #1 ASCI White (IBM RS/6000SP) 6MW power, 106 tons 12.3 TFlops 2013 Two MacPro workstation (dual AMD GPUs each) 14 TFlops
- 12. Deep Learning: Two Step Process Supercomputers used for training And then deploy the trained models everywhere! Datacenters Tablets, smartphones Wearable devices IoTs
- 13. Deep Learning: Training Big data + Deep learning + High performance computing = Intelligence Big data + Deep learning + Heterogeneous computing = Success
- 14. Image Recognition human vs. machine http://7-themes.com/6977111-cute-little-girl-play-white-dog.html
- 15. ImageNet Classification Challenge • ImageNet dataset • More than 15 million images belonging to about 22,000 categories • ILSVRC (ImageNet Large-Scale Visual Recognition Challenge) • Classification task: 1.2 million images contains 1,000 categories • One of the most challenging computer vision benchmarks • Increasing attention both from industry and academic communities * Olga Russakovsky et al. ECCV 2014
- 16. ImageNet Classification Challenge * courtesy of Feifei Li
- 18. ImageNet Classification 2012-2014 Team Year Place Error (top-5) Uses external data SuperVision 2012 - 16.4% no SuperVision 2012 1st 15.3% ImageNet 22k Clarifai 2013 - 11.7% no Clarifai 2013 1st 11.2% ImageNet 22k MSRA 2014 3rd 7.35% no VGG 2014 2nd 7.32% no GoogLeNet 2014 1st 6.67% no Slide credit: Yangqing Jia, Google Invincible ?
- 19. Latest Results
- 20. Latest Results Team Date Top-5 test error GoogLeNet 2014 6.66% Deep Image 01/12/2015 5.98% Deep Image 02/05/2015 5.33% Microsoft 02/05/2015 4.94% Google 03/02/2015 4.82% Deep Image 05/10/2015 4.58%
- 21. Insights and Inspirations 多算胜少算不胜 孙⼦子 计篇 (Sun Tzu, 544-496 BC) More calculations win, few calculation lose 元元本本殚⻅见洽闻 班固 ⻄西都赋(Gu Ban, 32-92 AD) Meaning the more you see the more you know 明⾜足以察秋毫之末 孟⼦子梁惠⺩王上 (Mencius, 372-289 BC) ability to see very fine details
- 22. Project Minwa (百度敏娲) • Minerva + Athena + ⼥女娲 • Athena: Goddess of Wisdom,Warfare, Divine Intelligence,Architecture, and Crafts • Minerva: Goddess of wisdom, magic, medicine, arts, commerce and defense • ⼥女娲: 抟⼟土造⼈人, 炼⽯石补天, 婚姻, 乐器 World’s Largest Artificial Neural Networks v Pushing the State-of-the-Art v ~ 100x bigger than previous ones v New kind of Intelligence?
- 23. Hardware/Software Co-design • Stochastic gradient descent (SGD) • High compute density • Scale up, up to 100 nodes • High bandwidth low latency • 36 nodes, 144 GPUs, 6.9TB Host, 1.7TB Device • 0.6 PFLOPS • Highly Optimized software stack • RDMA/GPU Direct • New data partition and communication strategies GPUs Infiniband
- 24. Minwa
- 25. Speedup (wall time for convergence) Validation set accuracy for different numbers of GPUs 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.25 0.5 1 2 4 8 16 32 64 128 256 Accuracy Time (hours) 32 GPU 16 GPU 1 GPU Accuracy 80% 32 GPU: 8.6 hours 1 GPU: 212 hours Speedup: 24.7x
- 26. Never have enough training examples! Key observations • Invariant to illuminant of the scene • Invariant to observers Augmentation approaches • Color casting • Optical distortion • Rotation and cropping etc Data Augmentation “⻅见多识⼲⼴广”
- 27. And the Color Constancy Key observations • Invariant to illuminant of the scene • Invariant to observers Augmentation approaches • Color casting • Optical distortion • Rotation and cropping etc The Color of the Dress “Inspired by the color constancy principal. Essentially, this ‘forces’ our neural network to develop its own color constancy ability.”
- 28. Data Augmentation Augmentation The number of possible changes Color casting 68920 Vignetting 1960 Lens distortion 260 Rotation 20 Flipping 2 Cropping 82944(crop size is 224x224, input image size is 512x512) Possible variations The Deep Image system learned from ~2 billion examples, out of 90 billion possible candidates.
- 29. Data Augmentation vs. Overfitting
- 30. Examples Bathtub Isopod Indian elephant Ice bear Some hard cases addressed by adding our data augmentation.
- 31. Multi-scale Training • Same crop size, different resolution • Fixed-size 224*224 • Downsized training images • Reduces computational costs • But not for state-of-the-art • Different models trained by different image sizes 256*256 512*512 • High-resolution model works • 256x256: top-5 7.96% • 512x512: top-5 7.42% • Multi-scale models are complementary • Fused model: 6.97% “明查秋毫”
- 33. Tricycle
- 34. Single Model Performance • One basic configuration has 16 layers • The number of weights in our configuration is 212.7M • About 40% bigger than VGG’s Team Top-5 val. error VGG 8.0% GoogLeNet 7.89% BN-Inception 5.82% MSRA, PReLU-net 5.71% Deep Image 5.40%
- 35. Robustness
- 40. Major Differentiators • Customized built supercomputer dedicated for DL • Simple, scalable algorithm + Fully optimized software stack • Larger models • More Aggressive data augmentation • Multi-scale, include high-resolution images Scalability + Insights and push for extreme
- 41. Deep Learning: Deployment Big data + Deep learning + High performance computing = Intelligence Big data + Deep learning + Heterogeneous computing = Success
- 42. Owl of Minwa (百度敏鸮) Supercomputers Datacenters Tablets, smartphones Models trained by supercomputers Trained models will be deployed in many ways data centers (cloud), smartphones, and even wearables and IoTs d OpenCL based, light weight and high performance DNNs everywhere ! knowledge, wisdom, perspicacity and erudition
- 43. DNNs Everywhere Supercomputers Datacenters Tablets, smartphones Wearable devices IoTs 1000s GPUs 100k-1m servers 2b (in China) 50b in 2020? Supercomputer used for training Trained DNNs then deployed to data centers (cloud), smartphones, and even wearables and IoTs
- 44. Offline Mobile DNN App • Image recognition on mobile device • Real time and no connectivity needed • directly from video stream, what you point is what you get • Everything is done within the device • OpenCL based, highly optimized • Large deep neural network models • Thousands of objects, flowers, dogs, and bags etc • Unleashed the full potential of the device hardware • Smart phones now, Wearables and IoTs tomorrow
- 46. Cloud Computing: What’s Missing? Bandwidth? Latency? and Power consumption? *ArtemVasilyev: CNN optimizations for embedded systems and FFT Moving data around is expensive, very expensive!
- 47. Cloud Computing: What’s Missing? How about privacy?
- 48. What’s Next? Dedicated Hardware + Heterogeneous Computing *MarkHorowitz
- 49. Heterogeneous Computing “Human mind and brain is not a single general-purpose processor but a collection of highly specialized components, each solving a different, specific problem and yet collectively making up who we are as human beings and thinkers. “ - Prof. Nancy Kanwisher
- 50. © Copyright Khronos Group 2015 - Page 50 Vision Processing Power Efficiency • Wearables will need ‘always-on’ vision - With smaller thermal limit / battery than phones! • GPUs have x10 imaging power efficiency over CPU - GPUs architected for efficient pixel handling • Dedicated Hardware/DSPs can be even more efficient - With some loss of generality • Mobile SOCs have space for more transistors - But can’t turn on at same time = Dark Silicon - Can integrate more gates ‘for free’ if careful how and when they are used PowerEfficiency Computation Flexibility Dedicated Hardware GPU Compute Multi-core CPU X1 X10 X100 Potential for dedicated sensor/vision silicon to be integrated into Mobile Processors But how will they be programmed for PORTABILITY and POWER EFFICIENCY?
- 51. © Copyright Khronos Group 2015 - Page 51 OpenCL Ecosystem Implementers Desktop/Mobile/FPGA Working Group Members Apps/Tools/Tests/Courseware Single Source C++ Programming Portable Kernel Intermediate Language Core API and Language Specs
- 52. Everything Connected Everything Intelligent Big data era AI era I2 oT Intelligent Internet of Things
- 53. Thank you!