This document discusses Intel's compiler optimizations. It states that Intel's compilers may optimize Intel microprocessors differently than non-Intel microprocessors. Some optimizations like SSE2, SSE3, and SSSE3 instructions are designed for Intel microprocessors. Intel does not guarantee the availability, functionality, effectiveness of optimizations on non-Intel microprocessors. The document advises checking product guides for specific instruction set coverage. It provides a notice revision date of August 4, 2011.

1. Jomar Silva
Technical Evangelist

2. Intel’s compilers may or may not optimize to the same degree for non-Intel
microprocessors for optimizations that are not unique to Intel microprocessors.
These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other
optimizations. Intel does not guarantee the availability, functionality, or
effectiveness or any optimization on microprocessors not manufactured by
Intel. Microprocessor-dependent optimizations in this product are intended for
use with Intel microprocessors. Certain optimizations not specific to Intel
microarchitecture are reserved for Intel microprocessors. Please refer to the
applicable product User and Reference Guides for more information regarding
the specific instruction sets covered by this notice. Notice Revision #20110804
2

3. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on
system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at www.intel.com.
Performance estimates were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as "Spectre" and
"Meltdown." Implementation of these updates may make these results inapplicable to your device or system.
Cost reduction scenarios described are intended as examples of how a given Intel-based product, in the specified circumstances and configurations, may affect future costs and
provide cost savings. Circumstances will vary. Intel does not guarantee any costs or cost reduction.
This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel
representative to obtain the latest forecast, schedule, specifications and roadmaps.
Any forecasts of goods and services needed for Intel’s operations are provided for discussion purposes only. Intel will have no liability to make any purchase in connection with
forecasts published in this document.
ARDUINO 101 and the ARDUINO infinity logo are trademarks or registered trademarks of Arduino, LLC.
Altera, Arria, the Arria logo, Intel, the Intel logo, Intel Atom, Intel Core, Intel Nervana, Intel Xeon Phi, Movidius, Saffron and Xeon are trademarks of Intel Corporation or its
subsidiaries in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others.
Copyright 2018 Intel Corporation.
3

4. This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel
representative to obtain the latest forecast, schedule, specifications and roadmaps.
Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at intel.com, or from the
OEM or retailer. No computer system can be absolutely secure.
Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance. Consult
other sources of information to evaluate performance as you consider your purchase. For more complete information about performance and benchmark results, visit
http://www.intel.com/performance.
Cost reduction scenarios described are intended as examples of how a given Intel-based product, in the specified circumstances and configurations, may affect future costs and
provide cost savings. Circumstances will vary. Intel does not guarantee any costs or cost reduction.
Statements in this document that refer to Intel’s plans and expectations for the quarter, the year, and the future, are forward-looking statements that involve a number of risks and
uncertainties. A detailed discussion of the factors that could affect Intel’s results and plans is included in Intel’s SEC filings, including the annual report on Form 10-K.
The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata
are available on request.
Performance estimates were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as "Spectre" and
"Meltdown." Implementation of these updates may make these results inapplicable to your device or system.
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
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.
Intel, the Intel logo, Pentium, Celeron, Atom, Core, Xeon, Movidius, Saffron and others are trademarks of Intel Corporation in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others.
© 2018 Intel Corporation.
4

5. Internet of
Things
Artificial
Intelligence
Connected &
Autonomous
World
5

6. Internet of
Things
Artificial
Intelligence
Connected &
Autonomous
World
6

7. 200% growth of information-based products & services by
2020 compared with traditional product & services¹
62% developers deem IoT ‘very important’
to digital strategies¹
1. IDC – Digital Transformation Predictions (source)
2. NLC – Cities and Innovation Economy: Perceptions of Local Leaders (source)
3. DataAge 2025, (link)
4. Forbes, December 10, 2017 (link)
>55% percentage of all data forecast to be
generated by IoT in 2025.³
>$300B annual B2B IoT revenue, led by industrial sector ⁴
66% of cities have invested in some type of smart
city technology²
7

8. Optimizing
Productivity
Driving
efficiency
Lowering
cost
Saving
Lives
Improving
QualityofLife
Growing
yield
increase
Security
Protectingthe
planet
8

9. CONNECTED SMART AUTONOMOUS
9

10. AnExplosionofDataandSophisticatedanalytics
gigabytes
per day
1.5 gigabytes
per minute
gigabytes
per minute
gigabytes
per minute
4020 200

11. 1. Amalgamation of analyst data and Intel analysis.
2. IDC FutureScape: Worldwide Internet of Things 2017 Predictions (link)
3. IDC FutureScape: Worldwide Internet of Things 2015 Predictions (link)
50%
45%
of data will be stored,
analyzed, and acted
on at the edge22018
2019
of IoT deployments will be
network constrained3
By2020
Average
internetuser 1.5GB data/day
Smart
hospital 3TB data/day
Autonomous
automobile 4TB data/day
Connected
airplane 40TB data/day
Smart
factory 1PB data/day
11
11

12. Things Network
Infrastructure
Data
Center/CloudEdgeCompute
12

13. SoftwareEnablement
CommonandSeamlessDeveloperExperience
Other names and brands may be claimed as the property of others.
Workload Accelerators Connectivity
13

14. AutonomousVehicles ResponsiveRetail Manufacturing
EmergencyResponse Financialservices MachineVision Cities/transportation
Publicsector
14

15. SmartCameras VideoGateways/NVRs Datacenter/Cloud
FPGAsolutionsfromintel
CV
Intel®MediaSDK, OPENVINOTM
Industry’sBroadestMediaandComputerVisionandDeepLearningPortfolio
15

16. Today
DigitalSecurity&Surveillance
2018+
ScalingtoIndustrial&Retail,EnabledbySWTools
OpenVINO™
16

17. Internet of
Things
Artificial
Intelligence
Connected &
Autonomous
World
17

18. DataAnalyticsneedsai
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive
Analytics
Cognitive
Analytics
Operational
Analytics
Advanced
Analytics
EmergingToday
Self-Learning and Completely
Automated Enterprise
Simulation-Driven Analysis
and Decision-Making
Foresight
What Will Happen, When, and Why
Insight
What Happened and Why
Hindsight
What Happened
AI
Datadeluge
COMPUTEbreakthrough
Innovationsurge

19. Consumer Health Finance Retail Government Energy Transport Industrial Other
Smart
Assistants
Chatbots
Search
Personalization
Augmented
Reality
Robots
Enhanced
Diagnostics
Drug
Discovery
Patient Care
Research
Sensory
Aids
Algorithmic
Trading
Fraud
Detection
Research
Personal
Finance
Risk Mitigation
Support
Experience
Marketing
Merchandising
Loyalty
Supply Chain
Security
Defense
Data
Insights
Safety &
Security
Resident
Engagement
Smarter
Cities
Oil & Gas
Exploration
Smart
Grid
Operational
Improvement
Conservation
Autonomous
Cars
Automated
Trucking
Aerospace
Shipping
Search &
Rescue
Factory
Automation
Predictive
Maintenance
Precision
Agriculture
Field
Automation
Advertising
Education
Gaming
Professional &
IT Services
Telco/Media
Sports
Source: Intel forecast
Aiwilltransform

20. Source: Forrester Research – Artificial Intelligence: Fact, Fiction. How Enterprises Can Crush It; What’s Possible for Enterprises in 2017
Aiadoptionisjustbeginning
58%
of business and technology
professionals said they're
researching AI, but only…
12%said they are currently
using AI systems.
In a recent Forrester Research survey…

21. Machinelearning DeepLearning
Example
Features
 Detect similarities &
anomalies in sea of data
 Large, diverse dataset
 Fully-explainable
 Real-time updates
 Practical to
‘reverse engineer’
 Tabular/limited dataset
 Good enough accuracy
 Fully-explainable
 Difficult problem to
‘reverse engineer’
 Large, uniform dataset
 Highest accuracy
Other
examples
Credit fraud detection
Issue and defect triage
Predictive maintenance
 Regression
 Anomaly detection
 Feature extraction
 Image/speech recognition
 Natural language
processing (NLP)
 Pattern detection
MULTIPLEapproachestoAI
Anti-Money
Laundering
Facial
recognition
Recommendation
engine
Cognitive
Reasoning
ANDMore…

22. Machine
Learning
How do you
engineer the best
features?
Machinelearning
𝑁 × 𝑁
Arjun
NEURAL NETWORK
𝒇 𝟏, 𝒇 𝟐, … , 𝒇 𝑲
Roundness of face
Dist between eyes
Nose width
Eye socket depth
Cheek bone structure
Jaw line length
…etc.
CLASSIFIER
ALGORITHM
SVM
Random Forest
Naïve Bayes
Decision Trees
Logistic Regression
Ensemble methods
𝑁 × 𝑁
Arjun
DeepLearning
How do you guide
the model to find
the best features?

23. Deeplearning:Trainingvs.inference
Lots of
labeled data!
Training
Inference
Forward
Backward
Model weights
Forward
“Bicycle”?
“Strawberry”
“Bicycle”?
Error
Human
Bicycle
Strawberry
??????
Data set size
Accuracy
Didyouknow?
Training requires a very large
data set and deep neural
network (i.e. many layers) to
achieve the highest accuracy
in most cases

24. Source: ILSVRC ImageNet winning entry classification error rate each year 2010-2016 (Left), https://www.microsoft.com/en-us/research/blog/microsoft-researchers-achieve-new-conversational-speech-recognition-milestone/ (Right)

25. Deeplearninginpractice
Source: Intel customer engagements
Source Data Inferencing Inference within broader application
Innovation
Cycle
Time-to-
Solution
15% 15% 23% 15% 15% 8% 8%
Experiment with
topologies
Tune hyper-
parameters
Document
resultsLabel data Load data Augment data
Support
inference
inputs
Compute-intensive
(Model Training)
Labor-intensive Labor-intensive
15%
15%
23%
15%
15%
8%
8%
Development
Cycle
∞ ∞
AI.Data
Processing
Decision
Process
Data
Integration &
Management
Broader
ApplicationDeploy RefreshProduction
Deployment
Data Store
Defect
detection

26. Edge
Device
ARTIFICIALINTELLIGENCE
Platforms Finance Healthcare Energy Industrial Transport Retail Home More…
Data Center
TOOLKITS
App
Developers
libraries
Data
Scientists
foundation
Library
Developers
*
*
*
*
FOR
* * * *
Hardware
IT System
Architects
Solutions
Solution
Architects
AI Solutions Catalog
(Public & Internal)
DEEPLEARNINGACCELERATORS
Inference
DEEPLEARNINGDEPLOYMENT
OpenVINO™ † Intel® Movidius™ SDK
Open Visual Inference & Neural Network Optimization
toolkit for inference deployment on CPU, processor
graphics, FPGA & VPU using TF, Caffe* & MXNet*
Optimized inference deployment
for all Intel® Movidius™ VPUs using
TensorFlow* & Caffe*
DEEPLEARNINGFRAMEWORKS
Now optimized for CPU Optimizations in progress
TensorFlow* MXNet* Caffe* BigDL/Spark* Caffe2* PyTorch* PaddlePaddle*
DEEPLEARNING
Intel® Deep
Learning Studio‡
Open-source tool to compress
deep learning development cycle
MACHINELEARNINGLIBRARIES
Python R Distributed
• Scikit-learn
• Pandas
• NumPy
• Cart
• Random
Forest
• e1071
• MlLib (on Spark)
• Mahout
ANALYTICS,MACHINE&DEEPLEARNINGPRIMITIVES
Python DAAL MKL-DNN
Intel distribution
optimized for
machine learning
Intel® Data Analytics
Acceleration Library
(for machine learning)
Open-source deep neural
network functions for
CPU, processor graphics
DEEPLEARNINGGRAPHCOMPILER
Intel® nGraph™ Compiler (Alpha)
Open-sourced compiler for deep learning model
computations optimized for multiple devices (CPU, GPU,
NNP) using multiple frameworks (TF, MXNet, ONNX)
AIFOUNDATION
A
R
T
I
F
I
C
I
A
l
I
N
T
E
L
L
I
G
E
n
C
e NNP L-1000
* * * *
Ai.intel.com
† Formerly the Intel® Computer Vision SDK
*Other names and brands may be claimed as the property of others.
All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice.

27. 27

28. Optimize/
Heterogeneous
Inference engine
supports multiple
devices for
heterogeneous flows.
(device-level
optimization)
Prepare
Optimize
Model optimizer:
 Converting
 Optimizing
 Preparing to
inference
(device agnostic,
generic optimization)
Inference
Inference engine
lightweight API
to use in
applications for
inference.
MKL-
DNN
cl-DNN
CPU: Intel®
Xeon®/Intel®
Core™/Intel Atom®
GPU
FPGA
Myriad™ 2/X
DLA
Intel®
Movidius™
API
Train
Train a DL model.
Currently supports:
 Caffe*
 Mxnet*
 TensorFlow*
 ONNX*
Extend
Inference engine
supports
extensibility
and allows
custom kernels
for various
devices.
Extensibility
C++
Extensibility
OpenCL™
Extensibility
OpenCL™/TBD
Extensibility
TBD
ApplicationdevelopmentwithOpenVINO™Toolkit

29. 29
Inference Engine
 Simple and unified API for inference
across all Intel® architecture
 Optimized inference on large Intel®
architecture hardware targets
(CPU/GEN/FPGA)
 Heterogeneous support allows
execution of layers across hardware
types
 Asynchronous execution improves
performance
 Futureproof/scale development for
future Intel® architecture processors
Inference Engine Common API
PluginArchitecture
Inference
Engine
Runtime
Intel®
Movidius™ API
Intel® Movidius™
Myriad™ 2
DLA
Intel Integrated
Graphics (GPU)
CPU: Intel® Xeon®/Intel®
Core™/Intel Atom®
clDNN Plugin
Intel® MKL-DNN
Plugin
OpenCL™Intrinsics*
FPGA Plugin
Applications/Service
Intel® Arria®
10 FPGA
Intel® Movidius™
Plugin

30. 30
Choosing the “Right” Hardware
Power/Performance Efficiency Varies
 Running the right workload on the
right piece of hardware  higher
efficiency
 Hardware acceleration is a must
 Heterogeneous computing?
Tradeoffs
 Power/performance
 Price
 Software flexibility, portability
PowerEfficiency
Computation Flexibility
Dedicated
Hardware
GPU
CPU
X1
X10
X100 Vision Processing
Efficiency
Vision DSPs
FPGA

31. 31

32. Internet of
Things
Artificial
Intelligence
Connected &
Autonomous
World
32

33. 33
Demo available at: https://intel.ly/2IVUMRp
Face Access Control Solution

34. 34
Internet
User
Interface
Gateway
Lock
MQTT
HTTP
Server 1
Application
Server 2
Front End
MQTT
Broker
MQTT

35. 35

36. Learn Develop Share
 Online tutorials
 Webinars
 Student kits
 Support forums
 Intel Optimized
Frameworks
 Exclusive access
to Intel® AI
DevCloud
 Project showcase
opportunities at
 Intel Developer
Mesh
 Industry &
Academic events
 Comprehensive
courseware
 Hands-on labs
 Cloud compute
 Technical Support
Teach
For developers, students, instructors and startups
Intel®AIacademy
software.intel.com/ai

37. Intel Distribution for OpenVINO: http://software.intel.com/openvino-toolkit
OpenVINO OpenSource: http://01.org/openvinotoolkit
Implementações de referência: https://intel.ly/2SwRigI
OpenVINO Workshop: https://bit.ly/2Eb6k3e
37

38. 38