Artificial Intelligence Enriched User Experience with ARM Technologies

Transcription

1 Artificial Intelligence Enriched User Experience with ARM Technologies Daniel Heo Senior Segment Manager Mobile, BSG, ARM ARM Tech Forum Singapore July 12 th 2017

2 Global AI survey: the world is ready 71 % see noticeable impact by % see better societal future 67 % want private data processed at the edge 37 % see medical/ scientific advances as biggest benefit 2

3 AI at the edge Advantages Lower latency Privacy Data locality Networking Mobile Wearables Challenge: Compute, memory & thermal constraints Timeline Healthcare 2017: Training in cloud & inference at edge AR/VR 80% inference, 20% training easily expected by 2025 IoT Automotive Challenge: Edge AI capability helps but system must still evolve to deal with data flow demands Robotics 4

4 Cortex-A75: New premium performance point Massive uplift for laptop-ready performance Smartphone power profile >50% more performance AI for edge to cloud compute compared to current devices Estimated device performance using SPECINT2006, final device results may vary Comparison using Cortex-A73 at 2.4GHz vs Cortex-A75 at 3GHz 5

5 Cortex-A55: Efficient performance redefined Breakthrough power efficiency Higher sustained performance 2.5x higher power efficiency More intelligent compute at the edge compared to current devices Comparison using Cortex-A53 in 28nm devices vs Cortex-A55 in 16nm devices 6

6 Mali-G72: Enabling tomorrow s technology today Optimized for next generation, advanced, real-world content 25% Higher energy efficiency* 17% ML efficiency gain*^ 20% Better area efficiency* High Fidelity Mobile Gaming Mobile VR Machine Learning on device 7 *Compared to Mali-G71, on same process node under similar conditions. ^based on GEMM ML algorithms

7 A system-level approach for Mobile AI Boosting AI performance with ARM new CPUs, GPU and compute library Mali GPU Cortex-A CPUs Accelerator 1.4x performance 17% GEMM efficiency gain More than 50x AI performance boost on the CPU in the next 3-5 years Up to 10x quicker response for accelerators 8

8 Mobile AI Use Cases Enhanced Human Interface Enhanced System / Security 9

9 Yes, We are Ready NOW! ARM Mobile Products for AI/ML Cortex-A Cortex-M Mali-G72 Security Mali-Vision DynamIQ Push for higher ST performance ISA for ML Dot Product Always-on DSP capabilities with M4/M7 40% Perf over G71 Best in class mobile ML efficiency gain Built in Security with CryptoCell product Accelerating DNN for image recognition SW Lib 5x - 15x in ML performance uplift 10

10 ARM Compute Library Optimized low-level functions for CPU and GPU Key imaging, vision and ML functions Ongoing integration with ML frameworks Enable faster deployment of CV and ML on ARM Targeting CPU (NEON) and GPU (OpenCL) Significant performance uplift compared to OSS alternatives Publically available Open-source (GitHub), MIT license, free Key Functions categories Basic arithmetic Convolutions Colour manipulation Feature detection Pyramids Filters Image reshaping Mathematical functions Neural network GEMM 11

11 Benchmarks cycle consumption breakdown 12

12 ARM Compute Library Faster than OpenCV Speed-up compared to equivalent NEON functions from OpenCV Tested on Huawei Mate 8 (Kirin 950, Octa-core, 2300 MHz, ARM Cortex-A72 and ARM Cortex-A53, 64-bit) Workload is single-core single-thread - DVFS disabled 13

13 Evaluating HOG/SVM performance OpenCV Compute Library 213.4ms Lower is better 95.4ms Image resolution 640x480 Speed-up 7.1 x 73.4ms 800x x 47.6ms 960x x 33ms 6.7ms 7.9ms 13.2ms 15.1ms 1280x x 640x x x x960 Speed-up compared to equivalent NEON functions from OpenCV Tested on Firefly board - CPU: ARM 4x Cortex-A17 1.8GHz, GPU: ARM Mali-T760 (MP4) 600 MHz 14

14 ARM Compute Library Faster than OpenCV Speed-up compared to equivalent NEON functions from OpenCV Tested on Huawei Mate 8 (Kirin 950, Octa-core, 2300 MHz, ARM Cortex-A72 and ARM Cortex-A53) Workload is single-core, single-thread, 64b - DVFS disabled 15

15 Demo: real-time food calories count 16

16 ThunderView: Compute Library improvements 10% faster 6% faster 126% faster Lower is better The ARM Compute Library is an excellent tool. Our engineers were able to improve performance of the critical path of our software, in particular convolution, using the ARM optimised CPU and GPU routines, and this has helped us achieve our performance targets and getting our solution to market sooner - Yu Yang, CEO of ThunderView 17

17 Sample of users of ARM Compute Library 18 UPDATED: 25 APRIL 2017

18 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 2017 ARM Limited