Dr. Fabio Baruffa Senior Technical Consulting Engineer, Intel IAGS Legal Disclaimer & Optimization Notice Performance results are based on testing as of September 2018 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit www.intel.com/benchmarks. INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS”. 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Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice. Notice revision #20110804 Optimization Notice Copyright © 2019, Intel Corporation. All rights reserved. 2 *Other names and brands may be claimed as the property of others. Legal notices & disclaimers This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps. 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Intel, the Intel logo, Pentium, Celeron, Atom, Core, Xeon, Movidius and others are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © 2019 Intel Corporation. Optimization Notice Copyright © 2019, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Outline ▪ Introduction to Intel® DL Boost ▪ Intel® AI optimized frameworks ▪ Integration with the popular AI/ML frameworks: ▪ Tensorflow accelerated with Intel® MKL-DNN ▪ Intel® Machine Learning Scaling Library ▪ Horovod ▪ Hands-on Optimization Notice Copyright © 2019, Intel Corporation. All rights reserved. 4 *Other names and brands may be claimed as the property of others. What is AI? Regression Supervised Classification AI Clustering Learning Decision Trees Machine Data Generation learning Image Processing unSupervised Speech Processing Learning Natural Language Processing Deep Recommender Systems learning Adversarial Networks Reinforcement Reinforcement Learning Learning No one size fits all approach to AI Optimization Notice Copyright © 2019, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Machine vs. Deep learning CLASSIFIER 푁 × 푁 풇ퟏ, 풇ퟐ, … , 풇푲 ALGORITHM Machine Roundness of face Dist between eyes SVM Learning Nose width Random Forest How do you Eye socket depth Naïve Bayes Arjun engineer the best Cheek bone structure Decision Trees features? Jaw line length Logistic Regression …etc. Ensemble methods NEURAL NETWORK Deep 푁 × 푁 Learning How do you guide Arjun the model to find the best features? Optimization Notice Copyright © 2019, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Deep learning glossary Library Framework Topology MKL-DNN DAAL Spark MlLib Scikit-Learn Intel® Mahout Distribution NumPy for Python Pandas Hardware-optimized Open-source software Wide variety of algorithms mathematical and other environments that facilitate deep modeled loosely after the human primitive functions that are learning model development & brain that use neural networks to commonly used in machine & deployment through built-in recognize complex patterns in deep learning algorithms, components and the ability to data that are otherwise difficult to topologies & frameworks customize code reverse engineer Translating common deep learning terminology Optimization Notice Copyright © 2019, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Deep learning breakthroughs Machines able to meet or exceed human image & speech recognition Image recognition Speech recognition 30% 30% 97% 23% person 23% 99% “play song” 15% 15% Error Error 8% 8% Human Human 0% 0% 2010 Present 2000 Present e.g. Tumor Document Oil & Gas Voice Defect Genome Detection Sorting search Assistant detection sequencing Source: ILSVRC ImageNet winning entry classification error rate each year 2010-2016 (Left), https://www.microsoft.com/en-us/research/blog/microsoft-researchers-achieve-new-conversational-speech-recognition-milestone/ (Right) Optimization Notice Copyright © 2019, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Deep learning Basics Training Human Did you know? Bicycle Forward Training with a large “Strawberry” data set AND deep (many layered) neural network ? “Bicycle” often leads to the highest accuracy inference Lots of Backward Error Strawberry labeled data! Model weights Inference Accuracy Forward “Bicycle”? ?????? Data set size Optimization Notice Copyright © 2019, Intel Corporation. All rights reserved. 10 *Other names and brands may be claimed as the property of others. Visit: www.intel.ai/technology Speed up development using open AI software Machine learning Deep learning TOOLKITS App Open source platform for building E2E Analytics & Deep learning inference deployment Open source, scalable, and developers AI applications on Apache Spark* with distributed on CPU/GPU/FPGA/VPU for Caffe*, extensible distributed deep learning TensorFlow*, Keras*, BigDL TensorFlow*, MXNet*, ONNX*, Kaldi* platform built on Kubernetes (BETA) Python R Distributed Intel-optimized Frameworks libraries * • Scikit- • Cart • MlLib (on Spark) * * And more framework Data learn • Random • Mahout optimizations underway • Pandas Forest including PaddlePaddle*, scientists * * • NumPy • e1071 Chainer*, CNTK* & others Intel® Intel® Data Analytics Intel® Math Kernel Kernels Distribution Acceleration Library Library Library for Deep Neural for Python* (DAAL) Networks (MKL-DNN) developers Intel distribution High performance machine Open source compiler for deep learning model optimized for learning & data analytics Open source DNN functions for computations optimized for multiple devices (CPU, GPU, machine learning library CPU / integrated graphics NNP) from multiple frameworks (TF, MXNet, ONNX) Optimization