VICE PRESIDENT, ARCHITECTURE GENERAL MANAGER, AI …...Batch Norm pooling normalization activation...
Transcript of VICE PRESIDENT, ARCHITECTURE GENERAL MANAGER, AI …...Batch Norm pooling normalization activation...
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VICE PRESIDENT, ARCHITECTURE GENERAL MANAGER,
AI PRODUCTS GROUP - INTEL
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Artificial intelligence
Machine learning
Deep learning
Types of analytics/ml (PARTIAL LIST)
C l a s s i f i c a t i o n
R e g r e s s i o n
C l u s t e r i n g
F e a t u r e l e a r n i n g
A n o m a l y d e t e c t i o n
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Sample-scale benchmarks
EARLYADOPTION
Compute ratioInference ~= Training
Programming ModelC++, Proprietary APIs
Hardware ArchitectureCPU, GPU
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At-scale deployment of science and real-life solutions
ML Frameworks‘Democratization of Data Science’
Compute ratioInference >> Training
EARLYMAJORITY
Hardware ArchitectureCPU, DL Acceleration/NNPs, GPUs
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End Devices to Datacenter
TCO-TOTAL COST OF OWNERSHIP
Performance vs Perf/W vs Latency
Integration with existing workloads
BLENDED REAL-LIFE AI WORKLOADS
Full platform optimization
BUILT FOR AI. DESIGNED TO SCALE.
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EcosystemCommunities
SOFTWARETools
HardwarePlatforms
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Vice President, AI Products GroupChief Technology Officer - Intel
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SOFTWARETools
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Convolution
Matrix multiplicationBatch Norm pooling
normalization
activation
HELPS REALIZE THE INCREDIBLE BENEFITS OF DIRECT OPTIMIZATION
Intel/mkl-dnn
OPEN SOURCE
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OPTIMIZING TENSORFLOW
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Use Case: Automated claims processing for health insurance
CUSTOMER IMPACT
Inferencing Performance on Intel® Xeon® Scalable using Intel distribution for Python* & Intel-optimized TensorFlow* enabled the arya platform to reduce claim processing time from 48hrs to 0.2 seconds
Performance with the Intel® Xeon® Scalable Processor
7
6
5
4
3
2
1
0Non-optimized
WorkloadOptimized Workload
Inference Performance
5.95x
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OPEN SOURCE COMPILER ENABLING FLEXIBILITYTO RUN MODELS ACROSS A VARIETY OF FRAMEWORKS AND HARDWARE
NervanaSystems/ngraph
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Akanksha BAlaniCountry Lead - Intel Software Developer Products
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Spark Core
FeatureParity
Lower TCO, improved ease of use
EfficientScale-Out
High Performance Deep Learning for Apache Spark* on CPU Infrastructure
No need to deploy costly accelerators, duplicate data, or suffer through scaling headaches!
Designed and Optimized for Intel® Xeon® Processor
Powered by Intel® MKL-DNN
Data Frame
ML Pipelines
SQL SparkR Streaming MLlib GraphX BigDL
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Result
Client:JD.Com, 2nd largest online retailer in China, ~ 250 M users.
Challenge: Building deep learning applications such as image similarity search without moving data.
Solution:Switched from GPU to CPU cluster. Using Apache Spark* with BigDL, running on Intel® Xeon® processors
Intel® Xeon® CPU
Processing ~380M images4X GaiN
https://software.intel.com/en-us/articles/building-large-scale-image-feature-extraction-with-bigdl-at-jdcom
*Other names and brands may be claimed as the property of others.
Intel does not control or audit third-party benchmark data or the web sites referenced in this document. You should visit the referenced web site and confirm whether referenced data are accurate.
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Emergency Response Financial services Machine Vision Cities/transportation
Autonomous Vehicles Responsive Retail Manufacturing Public sector
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VISUAL INFERENCING AND NEURAL NETWORK OPTIMIZATION
DEPLOY COMPUTER VISION AND DEEP
LEARNING CAPABILITIES TO THE EDGE
High Performance, high Efficiency for the edge
Write once + scale to Diverse Accelerators
Broad Framework support
Other names and brands may be claimed as the property of othersVPU = Vision Processing Unit (Movidius)
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HardwarePlatforms
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multi-watt to MILLIWATT
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Other names and brands may be claimed as the property of othersSource: https://research.fb.com/publications/applied-machine-learning-at-facebook-a-datacenter-infrastructure-perspective/
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OUTCOMESOLUTION DEPLOYEDVision
Performance Benchmarking in Image Detection & Recognition
Leveraging CNNs on Intel® Xeon® Platinum
Driving AI-empowered Business Services
Ensuring Adoption, Scale & ROI from AI Investments
320+Accounts
Analyst Reports Leadership 20 FY18
Automation Arbitrage19,000 people equivalent productivity
3,200+ Bots Deployed FY18
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Multiscale Convolution Neural NetworkIntel® MKL/MKL-DNN,
clDNN, DAAL
Optimized Libraries Intel® Omni-Path Architecture
Scaling of Time to TrainIntel® Omni-Path Architecture, Horovod and TensorFlow®
Sp
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om
pa
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ba
seli
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1.0
m
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sure
d in
tim
e t
o t
rain
in 1
no
de
s
1 Node 2 Nodes 4 Nodes 8 Nodes 1 Node 2 Nodes 4 Nodes 8 Nodes
TOTAL MEMORY USED192GB DDR4 PER INTEL® 2S XEON® 6148 PROCESSOR
128.6GB257.2GB
514.4GB
64.3GB
Other names and brands may be claimed as the property of others§ Configuration: CPU: Xeon 6148 @ 2.4GHz, Hyper-threading: Enabled. NIC: Intel® Omni-Path Host Fabric Interface, TensorFlow: v1.7.0, Horovod: 0.12.1, OpenMPI: 3.0.0. OS: CentOS 7.3, OpenMPU 23.0.0, Python 2.7.5Time to Train to converge to 99% accuracy in modelSoftware and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit http://www.intel.com/performance.
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http://aka.ms/[email protected]
A SCALABLE FPGA-POWERED SERVING PLATFORM FOR DEEP NEURAL NETWORKS
INDUSTRY-LEADING LATENCY (<2MS) AT ULTRA LOW COST
DEPLOYABLE TO INTELLIGENT CLOUD OR INTELLIGENT EDGE THROUGH AZURE IOT
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Deploy DNN and Computer Vision at the Edge
Native FP16 and Fixed Point 8 bit support
4 TOPS with 1 TOPS of DNN Compute at 1W
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Intel® Nervana™ NNP L-1000
in 2019
3-4x training performanceof first generationLake Crest product
PURPOSE BUILT DESIGN OPTIMIZED ACROSSMEMORY BANDWIDTH, UTILIZATION, AND POWER
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EcosystemCommunities
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Managing Director, Sales & Marketing Group Intel India
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• 3rd largest footprint of AI startups*
• 2nd largest developer ecosystem in the world
Community building critical to unleash the true potential of AI
• 1B+ citizen data digitized; payment digitization
• 4G infra momentum, content explosion
• Our demographics
• High friction verticals: Healthcare, Agriculture and Education
capabilities opportunitiesDigitization
* Tracxn Startup Tracker ** McKinsey Qtrly Data # Intel MS&F and Economic Times
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Personalized recommendations
Inventing entirely new
customer experiences
Fulfillment automation / inventory
management
Cargo Voice driven interactions
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Pre-built notebooks for
common problems
Built-in, high performance
algorithms
BUILD
One-click training
Hyperparameteroptimization
TRAIN
One-click deployment
Fully managed hosting with auto-
scaling
DEPLOY
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BIOMEDICAL IMAGE BASED ANALYTICS FOR DISEASE SCREENING/DIAGNOSIS
40X IMPROVEMENT IN INFERENCE PERFORMANCE
1
HEALTHCARE ANALYTICS WLs OPTIMIZATION ON
INTEL® XEON® PROCESSOR
2 3
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AN INTEL & IIIT-HYDERABAD INITIATIVE
Data makes ai possible
World’s First Public Data Set of Indian Driving Conditions
Enabling AI Research in Unstructured Driving Conditions & Innovations around Safe Mobility
Volume
Variety
Better Reasoning
and Actions
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EcosystemCommunities
SOFTWARETools
HardwarePlatforms
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Today’s presentation contains forward-looking statements.All statements made that are not historical facts are subjectto a number of risks and uncertainties, and actual results maydiffer materially. Please refer to our most recent earningsrelease, Form 10-Q and 10-K filing available on our websitefor more information on the risk factors that could causeactual results to differ.