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Gschwind - AI Everywhere: democratize AI with an open platform and end-to -end ecosystem
- 1. 1 AI Everywhere: Democratizing Artificial Intelligence withan open platform and end-to-end ecosystem Michael Gschwind, PhD Chief Architect, AI and Intelligent Systems Silicon Valley Computing Lab
- 2. 2 Artificial Intelligence Specific, human-like tasks Classifyand understand real world data: recognize objects, understand language, interpret data Machine-learning techniques Automate model build to iteratively learn from data and find hidden insights without programmer Self-learning After initial training and deployment, continue to learn on its own based on data it receives Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 3. 3 3 Big Data Artificial Intelligence & Intelligent Applications Machine Learning Deep Learning (Neural Nets) MichaelGschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 4. 4 The Time isRight Sufficient computing capacity for good models SuperVision (Alexnet) wins ILSVRC 2012 image recognition competition Finally more neurons than a fruit fly! Deep Neural Networks and many FLOPS! Deep Learning as a way to “automate” feature extraction! “traditional CV” Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 5. 5 Deep Learning Deep Learning ImageNet:5x improvement with Artificial Neural Networks (ANN) in 6 years Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 6. 6 Machine and DeepLearning What we do “without even thinking about it”…..we recognize a bicycle Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 7. 7 Neural Networks: “Inspired”by Biology Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 8. 8 Source: blog.openai.com/ai-and-compute/ AlexNet toAlphaGo Zero: 2x Performance Growth every 3.5 months Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 9. 9 Key Factors SupportingAI Improvements Algorithmic innovation Neural network architecture innovation; and improved algorithms for training Training data Improved collection, labeling and preprocessing of increasingly larger training corpora Compute capacity Steadily increasing compute capacity enables training of more sophisticated networks with more data Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 10. 10 Beyond Moore’s Law Moore’s Law Limitationsof device physics prevents continued exponential performance gains from chip manufacturing Architectural Innovation Improved efficiency of application-optimized accelerators improves performance and efficiency Hardware/Software Ecosystem Optimization Complimentary optimizations of the AI ecosystem improve application performance and developer productivity Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 11. 11 11 Gen 1: Acceleration PCIe-attached Accelerator Gen2: Multi-Accelerator Multi- and Many- Accelerator Systems Gen 3: Clustered Accelerators Clustered Many-Accelerator Systems with Optimized Communication Fabric Three Generations of Accelerated AI Systems Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 12. 12 The Real World:Big Data! 12 Source: Augmenting Pathology Labs with Big Data and Machine Learning (The Next Platform) Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 13. 13 AI Everywhere Michael Gschwind,AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 14. 14 for you by you AIEverywhere Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 15. 15 for you by you Endto End Interoperability Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 16. 16 Autonomous Intelligence Sustainable operation Reduce powerconsumption and network congestion Disconnected operation Fail-safe independent operation without network Real-time response Respond to local conditions without network delay Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 17. 17 Deploying DL Models MichaelGschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 18. 18 First AI Acceleratorin Mobile: Kirin 970 with NPU ML acceleration API and library for NPU API implemented as ML Mobile OS service Mobile OS assigns different tasks to purpose- optimized processing unit: NPU, GPU, CPU, etc. AI Applications AI Framework Library NPU (Neural Processing Unit) Ecosystem Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 19. 19 In-Device AI Acceleration •Incubate rich ecosystem with independent developers • Foster innovation to enable new AI use cases • Create end user choice through application diversity AI Applications AI Framework Library Create rich user-centric AI software ecosystem Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 20. 20 “Affordable AI” • Integratedin all devices to enable AI exploitation • Deliver AI performance for all users • Efficient resource usage for mobile and edge application AI Applications AI Framework Library Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 21. 21 Industry Initiative: MLPerf Industryinitiative to create standardized cross-platform, cross-vendor performance benchmark for Machine Learning Benchmark Suite Set of AI Benchmarks for AI Software Frameworks and Hardware Systems Model Build and Use Benchmarks for Model Build (Training) and Model Use (Inference), in the Cloud (Data Center) and at the Edge (Mobile, IoT). Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 22. 22 AI Model Patterns:In-Device Model In-Device AI model use In-Device model use pattern Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 23. 23 AI Model Patterns:In-Device Model In-Device AI model use Data upload In-Device model use pattern Model update Cluster training Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem Data storage
- 24. 24 AI Model Patterns:Cloud Model Cloud AI model use Data upload Cloud AI model use pattern Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 25. 25 AI Model Patterns:Cloud Model Cloud AI model use Data upload Cluster training Model update Cloud AI model use pattern Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem Data storage
- 26. 26 AI Model Patterns:Mobile Use Cases Combine In-Device and Cloud AI Model In-Device AI model use Data upload Hybrid In-Device/Cloud AI model use pattern Cloud AI model use Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 27. 27 AI Model Patterns:Mobile Use Cases Combine In-Device and Cloud AI Model In-Device AI model use Data upload Data storage Hybrid In-Device/Cloud AI model use pattern Cloud AI model use Model update Cluster training Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 28. 28 Information Technologies DefiningTheir Era PC Era Process and store data Internet Era Find and share data AI Era Understand data and discover information Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 29. 29 Many More BreakthroughsTo Come AlphaGo Zero AI trains itself using only Go rules: faster training than from past (human) games AlphaGo Teach AI coaches human players to become better by using AI-discovered knowledge AlphaGo AI learns from past games: long knowledge transfer (training) from past games 0 Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 30. 30 “AI is whateverhasn't been done yet” “Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation.’ ” (Rodney Brooks) 30 (Tesler’s Theorem) Michael Gschwind, AI Everywhere: Democratizing Artificial Intelligence with an open platform and end-to-end ecosystem
- 31. 31 AI Everywhere: democratize AIwith an open platform and end-to-end ecosystem user-focused open resilient sustainable from device to cloud