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How Mobile Devices Are Revolutionizing User Interaction HCI Korea 2015 | Seoul Neil Trevett | Khronos President NVIDIA Vice President Mobile Ecosystem

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How Mobile Devices Are Revolutionizing User Interaction HCI Korea 2015 | Seoul Neil Trevett | Khronos President NVIDIA Vice President Mobile Ecosystem How Mobile Devices are Revolutionizing User Interaction HCI Korea 2015 | Seoul Neil Trevett | Khronos President NVIDIA Vice President Mobile Ecosystem © Copyright Khronos Group 2014 - Page 1 Mobile and Advanced User Interaction • Mobile devices are evolving significant sensing capabilities - Sensors to gather information about the user and environment - Processing power to analyze and process the sensor data What are the What sensors are standards and APIs coming to mobile for developers to devices? access new sensor capabilities? What mobile acceleration capability Early examples of is developing to enabled devices process sensor data? and use cases © Copyright Khronos Group 2014 - Page 2 Mobile Computing Revolution • Mobile devices are a new platform for computer human interaction innovation - High market volume -> Investment $ -> lower cost and increasing functionality Announcement of new Pope in St. Peters Square © Copyright Khronos Group 2014 - Page 3 4 How Many Sensors in a Smartphone Today? • Ambient Light • RGB Light • Proximity • IR Gestures • 2 cameras • IR Autofocus Laser • 3 microphones • Touch • Position2 22 - GPS - WiFi (fingerprint) - Cellular (tri-lateration) - NFC, Bluetooth (beacons) • Pressure • Temperature • Humidity • Accelerometer Micro Electrical Mechanical Systems • Magnetometer ‘MEMS’ • Gyroscope © Copyright Khronos Group 2014 - Page 4 Mobile Camera – The Most Interesting Sensor? • Single sensor RGB cameras just the start of mobile visual revolution - Main focus has been on capturing accurate photographs – not vision processing - Vision processing lets us capture DATA not just PICTURES • New camera types : Stereo pairs -> Plenoptic array -> Depth cameras - Stereo disparity processing enables object scaling and depth extraction - Plenoptic arrays use FFTs and ray-casting to capture a light field - Structured Light sensors use image processing to extract depth from the distortion of an IR pattern projected onto the scene • Advanced sensor processing needs significant compute power - Vision processing can be effectively accelerated on GPUs today Dual Camera Plenoptic Array Capri Structured Light 3D Camera LG Electronics Pelican imaging PrimeSense © Copyright Khronos Group 2014 - Page 5 Mobile Photography -> Visual Computing Mobile Visual Computing Input = MEMS + Depth Camera Processors = ISP + CPU + GPU Result = Data for advanced user interaction and environment modeling Computational Photography Input = MEMS + 2D Camera Processors = ISP + CPU + GPU Result = Enhanced Images and Videos e.g. Panoramas Photography Processing Demands Processing Input = 2D Camera Processors = ISP + CPU Product = Static Images ISP = Image Signal Processor Dedicated hardware processor for processing camera imagery Time © Copyright Khronos Group 2014 - Page 6 Visual Computing = Graphics AND Vision Graphics Processing Data New mobile visual sensors for MORE DATA Advanced mobile hardware for MORE PROCESSING Enables closer intertwining of real and virtual worlds Imagery Vision Real time demo on CUDA-enabled laptop High-Quality Reflections, Refractions, and Caustics in Augmented Processing Reality and their Contribution to Visual Coherence P. Kán, H. Kaufmann, Institute of Software Technology and Interactive Systems, Vienna University of Technology, Vienna, Austria https://www.youtube.com/watch?v=i2MEwVZzDaA © Copyright Khronos Group 2014 - Page 7 Mobile Vision Acceleration = New Experiences Need for advanced sensors and the acceleration to process them Computational Face, Body and 3D Scene/Object Augmented Photography and Gesture Tracking Reconstruction Reality Videography © Copyright Khronos Group 2014 - Page 8 Mobile SOC Performance Increases Google Nexus 9 Xiaomi MiPad Shield Tablet Erista Maxwell GPU 100 Shield Portable Google Nexus 7 Tegra K1 Quad Cortex A15 HTC One X+ Kepler GPU 100x perf increase in four years Tegra 4 10 Quad Cortex A15 Tegra 3 Quad A9 Power saver 5th core SOC = ‘System On Chip’ Tegra 2 Dual A9 CPU/GPU AGGREGATE PERFORMANCE AGGREGATE CPU/GPU 1 2012 2013 2014 2015 2011 Device Shipping Dates © Copyright Khronos Group 2014 - Page 9 Mobile Thermal Design Point 10” Screen takes 1-2W Resolution makes a difference - Wearable AR 7” Screen the iPad3 screen takes up to 8W! Displays should takes 1W ideally remain cool 4-5” Screen takes to the touch and 250-500mW operate all day! 0.5W or less! 2-4W 4-7W 6-10W 30-90W Typical max system power levels before thermal failure Even as battery technology improves - these thermal limits remain © Copyright Khronos Group 2014 - Page 10 Power is the New Design Limit • The Process Fairy keeps bringing more transistors.. ..but the ‘End of Voltage Scaling’ means power is much more of an issue than in the past In the Good Old Days The New Reality Leakage was not important, and voltage Leakage has limited threshold voltage, scaled with feature size largely ending voltage scaling L’ = L/2 L’ = L/2 D’ = 1/L2 = 4D D’ = 1/L2 = 4D f’ = 2f f’ = ~2f V’ = V/2 V’ = ~V E’ = CV2 = E/8 E’ = CV2 = E/2 P’ = P P’ = 4P Halve L and get 4x the transistors and Halve L and get 4x the transistors and 8x the capability for 8x the capability for the same power 4x the power!! © Copyright Khronos Group 2014 - Page 11 How to Save Power? Write 32-bits to LP-DDR2 600pJ • Much more expensive to MOVE data than COMPUTE data Send 32-bits Off-chip • Process improvements WIDEN the gap 50pJ - 10nm process will increase ratio another 4X • Energy efficiency must be key metric during silicon AND app design - Awareness of where data lives, Send 32-bits 2mm where computation happens, 24pJ how is it scheduled 32-bit Float Operation For 40nm, 7pJ 1V process 32-bit Integer Add 1pJ 32-bit Register Write 0.5pJ © Copyright Khronos Group 2014 - Page 12 Hardware Saves Power e.g. Camera Sensor ISP • CPU - Single processor or Neon SIMD - running fast - Makes heavy use of general memory - Non-optimal performance and power • GPU - Programmable and flexible - Many way parallelism - run at lower frequency - Efficient image caching close to processors - BUT cycles frames in and out of memory • Camera ISP (Image Signal Processor) ~760 math Ops ~42K vals = 670Kb - Little or no programmability 300MHz ~250Gops - Data flows through compact hardware pipe - Scan-line-based - no global memory - Best perf/watt © Copyright Khronos Group 2014 - Page 13 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 X100 Dedicated Hardware GPU Potential for dedicated sensor/vision X10 Compute silicon to be integrated into Multi-core Mobile Processors Efficiency Power X1 CPU Computation Flexibility © Copyright Khronos Group 2014 - Page 14 Power Efficiency will Need Holistic App Design • Ultra-low power camera use cases will need smart use of all sensors in a device High-performance vision application processing - Computational videography - Face, body and gesture tracking Often/always on camera processing - Object and scene reconstruction to detect visual triggering events - Feature tracking, pose estimation e.g. for AR Minimum possible power for small repertoire of visual events 1 MIP sensor hub and Low power activation of camera and High-quality vision processing in accelerometers can detect processing to detect visual triggers vision-based applications device being used © Copyright Khronos Group 2014 - Page 15 Mobile Developers Need Help! Control, coordinate and Handle a diverse selection synchronize a diverse of emerging depth camera array of mobile sensors technologies Write maintainable code Write code that is deployable for a heterogeneous mix across multiple devices, of CPUs, GPUs and DSPs platforms and OS Leverage dedicated Create fluid 60Hz vision hardware for experiences on battery- minimized power powered mobile devices © Copyright Khronos Group 2014 - Page 16 Khronos Connects Software to Silicon Open Consortium creating ROYALTY-FREE, OPEN STANDARD APIs for hardware acceleration Defining the roadmap for low-level silicon interfaces needed on every platform Graphics, compute, rich media, vision, sensor and camera processing Rigorous specifications AND conformance tests for cross- vendor portability Acceleration APIs BY the Industry FOR the Industry Well over a BILLION people use Khronos APIs Every Day… © Copyright Khronos Group 2014 - Page 17 http://accelerateyourworld.org/ © Copyright Khronos Group 2014 - Page 18 Access to 3D on Over 2 BILLION Devices 1.9B Mobiles / year 300M Desktops / year Windows, Mac, Linux 1B Browsers / year Source: Gartner (December 2013) © Copyright Khronos Group 2014 - Page 19 OpenGL ES Momentum • OpenGL ES 3.1 is latest version and is standard in Android Lollipop - Announced at Google IO June 2014 • Google has defined Android Extension Pack (AEP) for premium Android gaming - Optional set of extensions for OpenGL ES 3.1 accessible through a single query - Functionality to support AAA games - Tessellation, Geometry shaders, ASTC Texture Compression • First OpenGL ES 3.1 drivers are shipping - Just a few months after specification Epic’s Rivalry demo using full Unreal Engine 4 Running in real-time on NVIDIA Tegra K1 with OpenGL ES 3.1 + AEP https://www.youtube.com/watch?v=jRr-G95GdaM © Copyright Khronos
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