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ARM and Machine Learning Jem Davies ARM Fellow & VP Technology Media Processing Group, Imaging & Vision Group December 12 2016 © ARM 2016 Who am I? Jem Davies - ARM Fellow and VP of Technology - Media Processing and Imaging & Vision Groups Working on multimedia, computer vision and machine learning 13 years at ARM Mathematician turned chemist, turned software engineer… 2 … turned architect … turned tech future predictor Glider pilot instructor, fireworks lighter and scuba diver… … what next? © ARM 2016 Machine Learning overview Machine Learning makes smart connections to previously encountered concepts It’s useful when: • • We don’t have algorithms… but we do have a lot of data Image recognition 3 © ARM 2016 Robotics Home security Speech recognition We use Machine Learning technology every day Speech recognition Computational photography Predictive text Face tracking camera 4 © ARM 2016 Digital assistant Fingerprint ID Definitions and recent developments 5 © ARM 2016 The AI landscape Machine Learning Constraints and control theory Artificial Intelligence Computer Vision AI Reasoning 6 © ARM 2016 Deep Learning Natural language processing Search and Optimization Machine Learning Algorithms: CNNs, RNNs, etc. Key terms and definitions Artificial Intelligence • The broadest term - applying to any technique enabling computers to mimic human intelligence Machine Learning • A subset of AI including techniques enabling computers to improve at tasks with experience. Includes deep learning Deep Learning/Neural Networks • A branch of machine learning that attempts to model real life ideas in data by using a deep graph with multiple processing layers Algorithms • DNNs, CNNs, RNNs, SGEMM etc. Computer Vision • An interdisciplinary field that allows computers to gain understanding from digital images or videos. Many computer vision applications use ML algorithms. 7 © ARM 2016 Recent developments in deep learning The image detection benchmark ImageNet has reached nearly 100% accuracy Research groups feel it’s not useful to work on ImageNet anymore (it’s a solved problem) 8 Largely due to Neural Networks GoogLeNet, Inception, etc. The successful approach used for ImageNet has spread to other domains, yielding great improvements © ARM 2016 Why did this happen ? The Machine Learning learning loop 9 © ARM 2016 Basic neural network Squashing function x1 x2 [Inputs] x3 xn-1 10 © ARM 2016 w2 w1 n w3 wn-1 xn Sigmoid: y=1/(1+e^(-z)) wn z = ∑ wi xi i =1 y = H ( z) y [Output] Machine Learning computation in the cloud and on-device 11 © ARM 2016 Dissecting the Machine Learning process Cloud side Training engine Generate inference engines Inference engine Heterogeneous tools Distribute & optimize Device side ARM Cortex + Mali + Others Platform acceleration libraries 12 © ARM 2016 Machine Learning process on ARM Cloud services / applications Cloud side Training engine Cloud service SDK + analytics Cloud Cloud applications Cloud APIs Metadata streams, thumbnails etc. Device Applications Inference engine Middleware Heterogeneous tools Device side ARM Cortex + Mali + Others Platform acceleration libraries 13 © ARM 2016 SW HW CPU GPU (Cache-coherent interconnect) CV/ML IP HW Cores HW Interconnect It is easy to recognise speech It is easy to wreck a nice beach 14 © ARM 2016 Automatic speech recognition is not easy Active research area for over 50 years! Applications 15 Significant attention recently due to large amount of cloud computing Neural Networks has improved performance Siri, Google Now, Cortana, Amazon Echo now creating usable applications Safety and security Interactions with smaller or hands free devices (e.g. wearables) Improved accessibility for hearing-impaired Allows indexing of spoken words © ARM 2016 ASR use cases and Machine Learning Large Vocabulary Continuous Speech Recognition (LVCSR) Keyword spotting of simple commands “OK Google”, “Set alarm for 7” Sound monitoring 16 Dictation/transcription, virtual assistant Requires dictionary, knowledge of grammar Early/automatic anomaly detection © ARM 2016 Keyword spotting Listen for certain words/phrases Only need to learn certain words “Okay Google” “Play music” Speech Signal Feature extraction Features Acoustic model Simpler algorithm Algorithm must run locally on device 17 No knowledge of grammar Only needs acoustic model Potentially always listening Power consumption vitally important © ARM 2016 Words Play music Computer Vision 18 © ARM 2016 Computer vision pipeline Output Application Pre-processing Feature extraction Detection and segmentation High-level processing Image acquisition ISP/GPU 19 © ARM 2016 GPU/CPU/DSP/Spirit CV CPU ARM and Machine Learning 20 © ARM 2016 Machine Learning runs on ARM-powered client devices today 21 © ARM 2016 Caffe framework deep learning on ARM Video hosted on youtube https://www.youtube.com/watch?v=k4ovpelG9vs&t=13s 22 © ARM 2016 Deep learning frameworks on ARM 23 DNN used for real-time detection of objects Based on the Caffe deep learning framework Detection entirely on the mobile device – not cloud based Optimized for ARM Cortex CPU or Mali GPU Running Machine Learning on the GPU frees up the CPU for other tasks Optimised libraries also being developed for TensorFlow and OpenVX © ARM 2016 2.7 64% 5.5 20% Conclusions Innovation Growth Maturity Machine Learning 24 The rapid uptake in Machine Learning is going mainstream, affecting compute everywhere Machine Learning dramatically increases compute demands As much as possible, Machine Learning workloads should run locally on device, not on remote servers Machine Learning is driving demand for advanced ARM processors and accelerator IP Machine Learning is having a significant impact on ARM’s roadmap for future processors and architectures © ARM 2016 Platforms & Tools Thank you for listening The trademarks featured in this presentation are registered and/or unregistered trademarks of ARM Limited (or its subsidiaries) in the EU and/or elsewhere. All rights reserved. All other marks featured may be trademarks of their respective owners. Copyright © 2016 ARM Limited © ARM 2016