artificalintelligence at the edge - bernsteinresearch.com 2017/02_arm.pdfupfront licence fee...
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© 2017 Arm Limited
Ian Thornton, Head of Investor Relations16 October 2017
Artifical intelligence at the edge
© 2017 Arm Limited 2
Arm introduction
Global leader in technology licensing
• R&D outsourcing for semiconductor companies
Innovative business model
• Upfront licence fee – flexible licensing models
• Ongoing royalties on partner sales
• Technology reused across multiple applications
Long-term, secular growth markets >1,480 licencesGrowing by >100 every year
>460 potentialroyalty payers
17.7 bn Arm-based chips shipped in 2016
~15% CAGR over previous 5 years
© 2017 Arm Limited 3
Arm technology is everywhereFrom sensors to smartphones to servers
22 years
4 years
4 years
20171990 2013 2021
50 billionchips shipped
50 billionchips shipped
100 billionchips expected to ship
© 2017 Arm Limited 4
Distributing intelligence everywhere
Robotics
Home, surveillance & analyticsIoT VR/MR
Mobile DronesAutomotive
Shipping & logistics
© 2017 Arm Limited 5
Bandwidth Power and Cost
From the cloud to the edge
Privacy LatencySecurity
© 2017 Arm Limited 6
AI is different for different applications
Smart
Home
Self D
riving C
ars/A
DAS
General Purpose/
Personal
Robotics
HealthMobile
Device/C
amera
Secu
rity
Industrial
Robotics
Detection (B
inary Present/N
ot Present)
Classific
ation (F
ew/broad cla
sses)
Recogn
ition (M
any s
pecific)
Motion Detec
tion
Motion Esti
mation/Trac
king
Pose Esti
mation
Segm
entation
Depth Perceptio
n
Egomotio
n Estimati
on
Super R
esolutio
n
Noise Reducti
on
Content Generat
ion/Remova
l
X X X X X X Anomoly/Event Detection X XX X X X Behavior Detection/Prediction X X X XX X X X X X Object Understanding X X X XX X X X X X Scene Understanding X X X X X X
X X X Navigation (Scene + Motion) - - - - - - X XX X X X Facial and/or Emotion Understanding X X
X X X Augmented Reality X X X XX X X X Image Capture/Enhancement X X X X X X X
© 2017 Arm Limited 7
Detection (B
inary Present/N
ot Present)
Classific
ation (F
ew/broad cla
sses)
Recogn
ition (M
any s
pecific)
Motion Detec
tion
Motion Esti
mation/Trac
king
Pose Esti
mation
Segm
entation
Depth Perceptio
n
Egomotio
n
Noise Reducti
on
Content Generat
ion/Remova
l
Viola-Jones/Haar Cascade XImage Histogram X X X
Template Matching (OCR) X XHistogram of Oriented Gradients (HoG) X X
Eigenfaces/Eigenvectors X X XKeypoint Features (SIFT/SURF/FAST/Harris) X X X X X
Convolutional Neural Networks X X XGMM Background/Foreground Segmentation X X
Sparse Optical Flow (Lucas Kanade) X X XDense Optical Flow (Gunner Farneback) X X X X X
Stereo depth perception (Block Matching) XRegistration (Homography, Stereo Matching) X X X
Clustering (k-means, EM, mean-shift) XGraph Cuts X XWatershed X
Deconvolutional Neural Network XFilters (Gaussian Blur, Median) X
Image Averaging XInpainting X
Seam Carving X
Families of AI algorithms
© 2017 Arm Limited 8
The spectrum of CV and ML algorithmsLots of data, simple computation per data item, data parallelism
Smaller amounts of highly structured data, complex computation per item
Convolution Color space conversion
Affine transforms
Feature detectors (variable)Block matching Optical flow
Support vector machines
K-means clustering
Feature matching
Neural networks
Parallel workloads:ISPs, GPUs
Serial workloads: CPUs
In-between workloads: DSPs
fixed-function IP, FPGA
© 2017 Arm Limited 9
Heterogeneity is necessary
There is no one size fits all solution
Need multiple types of processors to handle this
Fixed function works when requirements known in advance, algorithm is well understood, and high performance needed
Programmable cores are essential
DSP DSP DSP
DSP DSP DSP
DSP DSP DSP
CPU big CPU big FPGA
CPU Little
CPU Little DSP
GPU GPU Acc
© 2017 Arm Limited 10
Arm’s approach to AI
Machine Learning applications
Domain-specific libraries + ML frameworks: TensorFlow, Caffe, MXNet, etc.
Tools and libraries from Arm
CPUs + GPUs + ISPs + CV engines DSP FPGA ACC
© 2017 Arm Limited 11
ARM DynamIQ – Multicore redefined
New single cluster design Advanced compute capabilities
Redesigned memory sub-systemGreater flexibility
© 2017 Arm Limited 12
Accelerating AI adoption everywhere
DynamIQ boosting AI/ML performance both on CPU and in system
Improved access to acceleration
Up to 10x quicker response for accelerators
Dedicated processor instructions and optimized libraries for AI
More than 50x AI performance boost on the CPU in the next 3-5 years
1313
Thank You!Danke!Merci!谢谢!ありがとう!Gracias!Kiitos!
© 2017 Arm Limited