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1
Oct 2016
NVIDIA DEEP LEARNING
2
ENTERPRISE AUTOGAMING DATA CENTERPRO VISUALIZATION
THE WORLD LEADER IN VISUAL COMPUTING
3
THE BIG BANG IN MACHINE LEARNING
DNN GPUBIG DATA
100 hours of video
uploaded every
minute
350 millions
images uploaded
p...
4
BIG DATA & ANALYTICS
AUTOMOTIVE
Auto sensors reporting
location, problems
COMMUNICATIONS
Location-based advertising
CONS...
5
EXPONENTIAL DATA GROWTH
INCREASING DATA VARIETY
Search
Marketing
Behavioral
Targeting
Dynamic
Funnels
User
Generated
Con...
6
7
WHAT IS DEEP LEARNING?
ARTIFICAL
INTELLIGENCE MACHINE
LEARNING
DEEP LEARNINGPerception
Reasoning
Planning
Optimization
C...
8
DEEP LEARNING FUELING DISCOVERY
Classify Satellite Images for
Carbon Monitoring
Analyze Obituaries on the Web for
Cancer...
9
DEEP LEARNING FOR EVERY APPLICATION
Visual search for
e-commerce
Visual Search in
Geoinformatics
Improving Agriculture:
...
10
Language Classification
Deep Learning CNN
Super-Human Language Translation
DEEP LEARNING FOR EVERY APPLICATION
11
DEEP LEARNING FOR EVERY APPLICATION
12
CONSUMERS LOVE DEEP LEARNING
13
MORE THAN 1,500 AI START UPS
AROUND THE WORLD
Deep Learning
for Art
Deep Learning for
Cybersecurity
Deep Learning for
G...
14
IMAGENET CHALLENGE
Where it all started … again
bird
frog
person
hammer
flower pot
power drill
person
car
helmet
motorc...
15
ACHIEVING SUPERHUMAN PERFORMANCE
2012: Deep Learning
researchers
worldwide discover GPUs
2016: Microsoft achieves
speec...
16
DEEP LEARNING ADOPTION IS EXPONENTIAL
# of Organizations Using Deep Learning
Source: Jeff Dean, Spark Summit 2016
17
MASSIVE COMPUTING CHALLENGE
SPEECH RECOGNITION
2014
Deep Speech 1
80 GFLOP
7,000 hrs of Data
~8% Error
465 GFLOP
12,000...
18
Device
NVIDIA DEEP LEARNING PLATFORM
TRAINING
DIGITS Training System
Deep Learning Frameworks
Tesla P100, DGX1
DATACENT...
19
Device
NVIDIA DEEP LEARNING PLATFORM
TRAINING DATACENTER INFERENCING
Training: comparing to Kepler GPU in 2013 using Ca...
20
40x Efficient vs CPU, 8x Efficient vs FPGA
0
50
100
150
200
AlexNet
CPU FPGA 1x M4 (FP32) 1x P4 (INT8)
Images/Sec/Watt
...
21
INTRODUCING TESLA P100
Page Migration Engine
Virtually Unlimited Memory
CoWoS HBM2
3D Stacked Memory (i.e fast!)
NVLink...
22
NVIDIA DGX-1
AI Supercomputer-in-a-Box
170 TFLOPS | 8x Tesla P100 16GB | NVLink Hybrid Cube Mesh
2x Xeon | 8 TB RAID 0 ...
23
Instant productivity — plug-and-
play, supports every AI framework
Performance optimized across
the entire stack
Always...
24
NVIDIA POWERS DEEP LEARNING
Every major DL framework leverages NVIDIA SDKs
Mocha.jl
NVIDIA DEEP LEARNING SDK
COMPUTER V...
25
NVIDIA DIGITS
Interactive Deep Learning GPU Training System
Interactive deep neural network development
environment for...
26
NVIDIA cuDNN
Accelerating Deep Learning
High performance building blocks for deep learning
frameworks
Drop-in accelerat...
27
0 50 100 150 200 250 300
P40
P4
1x CPU (14 cores)
Inference Execution Time (ms)
11 ms
6 ms
User Experience: Instant Res...
28
NVIDIA DEEPSTREAM SDK
Delivering Video Analytics at Scale
Inference
Preprocess
Hardware
Decode
“Boy playing soccer”
Sim...
29
“Billions of intelligent devices will take advantage of deep learning to provide
personalization and localization as GP...
30
SMART CITIES OF THE FUTURE
“Pittsburgh's "predictive policing" program … police car laptops will display maps
showing l...
31
ACCELERATED ANALYTICS TECHNOLOGY
32
GPU-ACCELERATION HAS NO LIMITS
MapD
MapD is 55x to 1,000x faster than
comparable CPU databases on billion+
row datasets...
33
MASSIVE SCALE GPU ACCELERATED ANALYTICS
DEA theft of Silk Road bitcoinsSIEM attack escalationTwitter botnet deconstruct...
34
GETTING STARTED WITH DEEP LEARNING
developer.nvidia.com/deep-learning
35
Thank you!
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Introduction to Deep Learning (NVIDIA)

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NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.

https://tech.rakuten.co.jp/

Published in: Technology

Introduction to Deep Learning (NVIDIA)

  1. 1. 1 Oct 2016 NVIDIA DEEP LEARNING
  2. 2. 2 ENTERPRISE AUTOGAMING DATA CENTERPRO VISUALIZATION THE WORLD LEADER IN VISUAL COMPUTING
  3. 3. 3 THE BIG BANG IN MACHINE LEARNING DNN GPUBIG DATA 100 hours of video uploaded every minute 350 millions images uploaded per day 2.5 Petabytes of customer data hourly 0.0 0.5 1.0 1.5 2.0 2.5 3.0 2008 2009 2010 2011 2012 2013 2014 NVIDIA GPU x86 CPU TFLOPS
  4. 4. 4 BIG DATA & ANALYTICS AUTOMOTIVE Auto sensors reporting location, problems COMMUNICATIONS Location-based advertising CONSUMER PACKAGED GOODS Sentiment analysis of what’s hot, problems $ FINANCIAL SERVICES Risk & portfolio analysis New products EDUCATION & RESEARCH Experiment sensor analysis HIGH TECHNOLOGY / INDUSTRIAL MFG. Mfg. quality Warranty analysis LIFE SCIENCES Clinical trials MEDIA/ENTERTAINMENT Viewers / advertising effectiveness ON-LINE SERVICES / SOCIAL MEDIA People & career matching HEALTH CARE Patient sensors, monitoring, EHRs OIL & GAS Drilling exploration sensor analysis RETAIL Consumer sentiment TRAVEL & TRANSPORTATION Sensor analysis for optimal traffic flows UTILITIES Smart Meter analysis for network capacity, LAW ENFORCEMENT & DEFENSE Threat analysis - social media monitoring, photo analysis
  5. 5. 5 EXPONENTIAL DATA GROWTH INCREASING DATA VARIETY Search Marketing Behavioral Targeting Dynamic Funnels User Generated Content Mobile Web SMS/MMS Sentiment HD Video Speech To Text Product/ Service Logs Social Network Business Data Feeds User Click Stream Sensors Infotainment Systems Wearable Devices Cyber Security Logs Connected Vehicles Machine Data IoT Data Dynamic Pricing Payment Record Purchase Detail Purchase Record Support Contacts Segmentation Offer Details Web Logs Offer History A/B Testing BUSINESS PROCESS PETABYTESTERABYTESGIGABYTESEXABYTESZETTABYTES Streaming Video Natural Language Processing WEB DIGITAL AI 90% of the world’s data created in the last year - IBM
  6. 6. 6
  7. 7. 7 WHAT IS DEEP LEARNING? ARTIFICAL INTELLIGENCE MACHINE LEARNING DEEP LEARNINGPerception Reasoning Planning Optimization Computational Statistics Supervised and Unsupervised Learning Neural networks Distributed Representations Hierarchical Explanatory Factors Unsupervised Feature Engineering
  8. 8. 8 DEEP LEARNING FUELING DISCOVERY Classify Satellite Images for Carbon Monitoring Analyze Obituaries on the Web for Cancer-related Discoveries Determine Drug Treatments to Increase Child’s Chance of Survival NASA AMES
  9. 9. 9 DEEP LEARNING FOR EVERY APPLICATION Visual search for e-commerce Visual Search in Geoinformatics Improving Agriculture: LettuceBot only sprays weeds
  10. 10. 10 Language Classification Deep Learning CNN Super-Human Language Translation DEEP LEARNING FOR EVERY APPLICATION
  11. 11. 11 DEEP LEARNING FOR EVERY APPLICATION
  12. 12. 12 CONSUMERS LOVE DEEP LEARNING
  13. 13. 13 MORE THAN 1,500 AI START UPS AROUND THE WORLD Deep Learning for Art Deep Learning for Cybersecurity Deep Learning for Genomics Deep Learning for Self-Driving Cars
  14. 14. 14 IMAGENET CHALLENGE Where it all started … again bird frog person hammer flower pot power drill person car helmet motorcycle person dog chair 1.2M training images • 1000 object categories Challenge
  15. 15. 15 ACHIEVING SUPERHUMAN PERFORMANCE 2012: Deep Learning researchers worldwide discover GPUs 2016: Microsoft achieves speech recognition milestone 2015: ImageNet — Deep Learning achieves superhuman image recognition
  16. 16. 16 DEEP LEARNING ADOPTION IS EXPONENTIAL # of Organizations Using Deep Learning Source: Jeff Dean, Spark Summit 2016
  17. 17. 17 MASSIVE COMPUTING CHALLENGE SPEECH RECOGNITION 2014 Deep Speech 1 80 GFLOP 7,000 hrs of Data ~8% Error 465 GFLOP 12,000 hrs of Data ~5% Error 2015 Deep Speech 2 10X Training Ops IMAGE RECOGNITION 2012 AlexNet 8 Layers 1.4 GFLOP ~16% Error 152 Layers 22.6 GFLOP ~3.5% Error 2015 ResNet 16X Model
  18. 18. 18 Device NVIDIA DEEP LEARNING PLATFORM TRAINING DIGITS Training System Deep Learning Frameworks Tesla P100, DGX1 DATACENTER INFERENCING DeepStream SDK TensorRT Tesla P40 & P4
  19. 19. 19 Device NVIDIA DEEP LEARNING PLATFORM TRAINING DATACENTER INFERENCING Training: comparing to Kepler GPU in 2013 using Caffe, Inference: comparing img/sec/watt to CPU: Intel E5-2697v4 using AlexNet 65Xin 3 years Tesla P100 40Xvs CPU Tesla P4
  20. 20. 20 40x Efficient vs CPU, 8x Efficient vs FPGA 0 50 100 150 200 AlexNet CPU FPGA 1x M4 (FP32) 1x P4 (INT8) Images/Sec/Watt Maximum Efficiency for Scale-out Servers TESLA P4 5.5 TFLOPS 0 20,000 40,000 60,000 80,000 100,000 GoogLeNet AlexNet 8x M40 (FP32) 8x P40 (INT8)TESLA P40 Highest Throughput for Scale-up Servers Images/Sec 4x Boost in Less than One Year
  21. 21. 21 INTRODUCING TESLA P100 Page Migration Engine Virtually Unlimited Memory CoWoS HBM2 3D Stacked Memory (i.e fast!) NVLink GPU Interconnect for Maximum Scalability
  22. 22. 22 NVIDIA DGX-1 AI Supercomputer-in-a-Box 170 TFLOPS | 8x Tesla P100 16GB | NVLink Hybrid Cube Mesh 2x Xeon | 8 TB RAID 0 | Quad IB 100Gbps, Dual 10GbE | 3U — 3200W
  23. 23. 23 Instant productivity — plug-and- play, supports every AI framework Performance optimized across the entire stack Always up-to-date via the cloud Mixed framework environments —containerized Direct access to NVIDIA experts DGX STACK Fully integrated Deep Learning platform
  24. 24. 24 NVIDIA POWERS DEEP LEARNING Every major DL framework leverages NVIDIA SDKs Mocha.jl NVIDIA DEEP LEARNING SDK COMPUTER VISION SPEECH & AUDIO NATURAL LANGUAGE PROCESSING OBJECT DETECTION IMAGE CLASSIFICATION VOICE RECOGNITION LANGUAGE TRANSLATION RECOMMENDATION ENGINES SENTIMENT ANALYSIS
  25. 25. 25 NVIDIA DIGITS Interactive Deep Learning GPU Training System Interactive deep neural network development environment for image classification and object detection Schedule, monitor, and manage neural network training jobs Analyze accuracy and loss in real time Track datasets, results, and trained neural networks Scale training jobs across multiple GPUs automatically
  26. 26. 26 NVIDIA cuDNN Accelerating Deep Learning High performance building blocks for deep learning frameworks Drop-in acceleration for widely used deep learning frameworks such as Caffe, CNTK, Tensorflow, Theano, Torch and others Accelerates industry vetted deep learning algorithms, such as convolutions, LSTM, fully connected, and pooling layers Fast deep learning training performance tuned for NVIDIA GPUs Deep Learning Training Performance Caffe AlexNet Speed-upofImages/SecvsK40in2013 K40 K80 + cuDN… M40 + cuDNN4 P100 + cuDNN5 0x 10x 20x 30x 40x 50x 60x 70x 80x “ NVIDIA has improved the speed of cuDNN with each release while extending the interface to more operations and devices at the same time.” — Evan Shelhamer, Lead Caffe Developer, UC Berkeley AlexNet training throughput on CPU: 1x E5-2680v3 12 Core 2.5GHz. 128GB System Memory, Ubuntu 14.04 M40 bar: 8x M40 GPUs in a node, P100: 8x P100 NVLink-enabled
  27. 27. 27 0 50 100 150 200 250 300 P40 P4 1x CPU (14 cores) Inference Execution Time (ms) 11 ms 6 ms User Experience: Instant Response 45x Faster with Pascal + TensorRT Faster, more responsive AI-powered services such as voice recognition, speech translation Efficient inference on images, video, & other data in hyperscale production data centers INTRODUCING NVIDIA TensorRT High Performance Inference Engine 260 ms Training Device Datacenter
  28. 28. 28 NVIDIA DEEPSTREAM SDK Delivering Video Analytics at Scale Inference Preprocess Hardware Decode “Boy playing soccer” Simple, high performance API for analyzing video Decode H.264, HEVC, MPEG-2, MPEG-4, VP9 CUDA-optimized resize and scale TensorRT 0 20 40 60 80 100 1x Tesla P4 Server + DeepStream SDK 13x E5-2650 v4 Servers ConcurrentVideoStreams Concurrent Video Streams Analyzed
  29. 29. 29 “Billions of intelligent devices will take advantage of deep learning to provide personalization and localization as GPUs become faster and faster over the next several years.” — Tractica BILLIONS OF INTELLIGENT DEVICES
  30. 30. 30 SMART CITIES OF THE FUTURE “Pittsburgh's "predictive policing" program … police car laptops will display maps showing locations where crime is likely to occur, based on data-crunching algorithms developed by scientists at Carnegie Mellon University — Science
  31. 31. 31 ACCELERATED ANALYTICS TECHNOLOGY
  32. 32. 32 GPU-ACCELERATION HAS NO LIMITS MapD MapD is 55x to 1,000x faster than comparable CPU databases on billion+ row datasets Kinetica Hardware costs that are 1⁄10 that of standard in-memory databases BlazeGraph 200-300x speed-up Graphistry See 100x more data at millisecond speed SQream The supercomputing powers of the GPU combined with SQream’s patented technology, results in up to 100 times faster analytics performance on terabyte- petabyte scale data sets
  33. 33. 33 MASSIVE SCALE GPU ACCELERATED ANALYTICS DEA theft of Silk Road bitcoinsSIEM attack escalationTwitter botnet deconstruction
  34. 34. 34 GETTING STARTED WITH DEEP LEARNING developer.nvidia.com/deep-learning
  35. 35. 35 Thank you!

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