Embedded Image and Vision Processing

Transcription

1 From Technologies to Market Embedded Image and Vision Processing Sample November 2017

2 ABOUT THE AUTHORS Biographies and Contacts Yohann Tschudi As a Software & Market Analyst, Dr. Yohann Tschudi is a member of the MEMS & Sensors business unit at Yole Développement (Yole). Yohann is daily working with Yole s analysts to identify, understand and analyze the role of the software parts within any semiconductor products, from the machine code to the highest level of algorithms. Market segments especially analyzed by Yohann include big data analysis algorithms, deep/machine learning, genetic algorithms, all coming from Artificial Intelligence (IA) technologies. After his thesis at CERN (Geneva, Switzerland) in particle physics, Yohann developed a dedicated software for fluid mechanics and thermodynamics applications. Afterwards, he served during 2 years at the University of Miami (FL, United-States) as a research scientist in the radiation oncology department. He was involved in cancer auto-detection and characterization projects using AI methods based on images from Magnetic Resonance Imaging (MRI). During his research career, Yohann has authored and co-authored more than 10 relevant papers. Yohann has a PhD in High Energy Physics and a master degree in Physical Sciences from Claude Bernard University (Lyon, France). tschudi@yole.fr Embedded software in vision systems Sample 2

3 TABLE OF CONTENTS REPORT OBJECTIVES AND METHODOLOGY 5 1 INTRODUCTION 12 Software Embedded software 2 IMAGE PROCESSING 23 Definition Basics : Primary ISP architecture for Bayer image sensor Image processing to recover artefacts Image processing for color reproduction Image processing with pre-/post-processing Conclusion 3 ARTIFICIAL INTELLIGENCE 42 Definition A modern history of AI AI technologies Discussion 4 PLAYERS AND MARKET DYNAMICS 59 ISP & vision processor AI in vision systems CONCLUSIONS Embedded software in vision systems Sample 3

4 SCOPE OF THE REPORT L e n s Image sensor Dead pixel concealme nt Black level compensati on Lens shading correction Image signal processor and vision processor White balance Demosaicin g Gamma correction Color correction Noise filter for luma Color space conversion Face recognition Edge enhanceme nt Noise filter for chroma Dual cameras Face detection Contrast brightness control Hue saturation control Video stabilization Data formatter Not shaded technologies are not treated in this report Eye tracking Iris recognition HDR 2 domains are considered in this report Behavior recognition Gesture recognition Free space Human machine interface Technologies Computational photography Super resolution Digital zoom Deep learning Artificial intelligence in vision systems Traffic sign detection Automotive Machine vision AR/VR Pedestrian detection SLAM Lane departure Forward collision warning 3D reconstruction Video analytics Embedded software in vision systems Sample 4

5 COMPANIES CITED IN THIS REPORT Algolux, Altek, Ambarella, AMD, ARM, Apple, CEVA, Canon, Chips&Media, DNNresearch, DeepMind, Emotient, Fotonation, Google, Intel, Imagination, Lattice, Microsoft, Mobileye, Movidius, Nextchip, Nvidia, NXP, Omnivision, ON Semiconductors, Pixelworks, Qualcomm, Realface, Socionext, Sony, STMicroelectronics, Sunplus, Synopsys, Xperi Embedded software in vision systems Sample 5

6 SOFTWARE Set of sequences of instructions (programs) that can be interpreted by a machine and a set of data necessary for these operations General definition Analogy Hardware Software Embedded software in vision systems Sample 6

7 SOFTWARE LICENSE Public domain Non protective FOSS* license Protective FOSS* license Freeware Shareware Freemium Proprietary license Trade secret More rights granted Less rights granted Copyright retained No Yes Yes Yes Yes Yes Type of licensing for software Perform and display Yes Yes Yes Yes Yes No Copy Yes Yes Yes Often No No Modify Yes Yes Yes No No No Distribute Yes Yes, under same license Yes, under same license Often No No Sublicense Yes Yes No No No No Examples SERVER-SIDE The Google search algorithm *Free and Open Source Software Embedded software in vision systems Sample 7

8 EMBEDDED SOFTWARE Global definition Embedded software is used to control the limited and set functions of hardware devices. It is not typically worked with directly by users. Its functions are activated by external controls, either external actions of the device itself or remote input. The device may have communication links to other devices for functionality or in case the device needs to be adjusted, calibrated or diagnosed. It is written specifically for the particular hardware that it runs on and usually has processing and memory constraints because of the device s limited computing capabilities. Examples of embedded software include those found in dedicated GPS devices, factory robots, some calculators and even modern smartwatches. Embedded software varies in complexity as much the devices it is used to control. Although the term is often used interchangeably with firmware, embedded software is often the only computer code running on a piece of hardware, while firmware, in contrast, hands over control to an operating system that in turn launches and controls programs. In this report we will focus on two types of embedded software in vision systems : Image processing Very close to hardware Inference software from AI s technics High level algorithms Embedded software in vision systems Sample 8

9 IMAGE PROCESSING PIPELINE L e n s Image sensor Dead pixel concealment Black level compensation Lens shading correction ISP blocks details White balance Demosaicing Gamma correction Color correction Color space conversion Noise filter for chroma Hue saturation control Data formatter Noise filter for luma Edge enhancement Contrast brightness control Embedded software in vision systems Sample 9

10 ISP & VISION PROCESSOR MARKET SHARES AND EVOLUTIONS Some clues on what happening for the hardware segment IP and hardware Embedded software in vision systems Sample 10

11 AI IN VISION SYSTEMS The main goal of AI is for an artificial system to be autonomous. For that, understanding the environment an navigate through it takes a large part of the research. Because 80% of the human understanding of the world pass through eyes, vision systems share with AI one main research topic : Focus on AI technologies in vision systems Embedded software in vision systems Sample 11

12 DEEP LEARNING Deep learning is part of machine learning methods. It is based on deep neural networks which are ANN with multiple hidden layers between the input and output layers. In computer vision, DL is used for object recognition. Although CNNs trained by backpropagation had been around for decades, and GPU implementations of ANNs for years, including CNNs, fast implementations of CNNs with max-pooling on GPUs and high level memory were needed to progress on computer vision. The most powerful method in vision systems Example of DNN used for face recognition during the training phase Embedded software in vision systems Sample 12

13 AI RELATED TO VISION SYSTEMS GLOBAL CHARACTERISTICS Very high High Medium Image processing Face detection Eye tracking Iris recognition Face recognition Object detection Free space recognition Gesture recognition Object recognition Behavior recognition Low CPU requirements Not relevant Memory requirements Main characteristics of the embedded inference software technologies in vision systems Resolution requirements Execution speed requirements Algorithm complexity Training requirements Range <10m <1m <1m <1m <100m <3m <3m <50m 5-20m Learn in usage Yes Yes No Yes No No No No No Input from Photo (P) or Video (V) P/V P V P P V V V V V Object analysis Scene analysis Embedded software in vision systems Sample 13

14 EMERGENCE OF AI TECHNOLOGICAL FACTORS Technological factors implied in the emergence of AI in vision systems and forecast Embedded software in vision systems Sample 14

15 RELATED REPORTS Embedded software in vision systems Sample 15

16 EMBEDDED IMAGE AND VISION PROCESSING Market & Technology report - November 2017 From algorithms included in the image processing pipeline to neural networks running in vision processors, focus on the evolution of hardware in vision systems and how software disrupts this domain. KEY FEATURES OF THE REPORT Get the sample of the report on Image processing technology description Description of Artificial Intelligence (AI) technology in vision systems Intellectual property, hardware and business models for the image signal processor and vision processor market AI technology and business models in the vision system ecosystem Technology trends and future outlook OBJECTIVES OF THE REPORT Understand the technologies Image processing pipeline AI technologies based on neural networks Understand the evolution of hardware Business models Image signal processor to vision processors to sensor fusion Market share and evolution Understand how software is disrupting hardware and future trends SOFTWARE IN VISION SYSTEMS Vision systems are becoming increasingly important. Therefore, this report shows and explains the close links between embedded software and hardware in vision systems at the technology and market levels. What are the software technologies? How do they impact the hardware? Which hardware is impacted? What kinds of markets are affected? And how will they evolve? We can consider software in vision systems as having two different levels. The first is very close to the hardware, inscribed inside standalone field programmable gate array (FPGA) or application specific integrated circuit (ASIC) chips, or integrated into more complicated architectures. This layer, not often considered, is the most important step in any image treatment after image acquisition by pixels. Image processing, realized in the image signal processor (ISP), has a quite simple function. It must transform a signal from the sensor to an understandable image for the human eye. It is composed as a pipeline of multiple blocks, where each block takes as input the output of the previous block. A lot of different algorithms are implemented to accomplish tasks such as removing artefacts, color correction and reproduction. This is done at a single-pixel or pixel-group level and does not need a lot of memory or power. The second software layer is completely different, with much more diverse and complicated functions. In this report, we focused on embedded software and, more precisely, inference software derived from the latest artificial intelligence (AI) methods. This kind of technology necessitates a lot of memory and computing power. It uses complete image frames as input, and its goal is not to correct, but to analyze and understand the world in the picture. In vision systems, AI technology focuses on detection of eyes, faces, traffic signs, pedestrians, lanes, objects in front of cars and free space, and recognition of faces, irises, behaviors and gestures based on a mathematical technique called a neural network. This report especially investigates one of the most famous technologies, which has given spectacular results in recent years: deep learning. Artificial intelligence technologies in vision systems Face Face detection recognition Iris recognition Dual cameras Video stabilization HDR Behavior recognition Gesture recognition Human machine interface Computational photography Super resolution Digital zoom Free space Traffic sign detection Automotive Machine vision Deep learning AR/VR Pedestrian detection Lane departure (Yole Développement, November 2017) Forward collision warning Video analytics 3D reconstruction SLAM The shaded technologies are addressed in this report

17 EMBEDDED IMAGE AND VISION PROCESSING WHEN SOFTWARE DISRUPTS HARDWARE AI has completely disrupted hardware in vision systems, and has had an impact on entire segments, like Mobileye has in automotive, for example. Image analysis adds a lot of value and From image signal processor to sensor fusion processor Amount of data processed Set of pixels processing ISP Image processing algorithms Is part of Standalone ISP from Altek (Yole Développement, November 2017) Frame processing Vision processor Vision processor from Mobileye Introduction of AI algorithms Frame processing + other sensors Sensor fusion processor Sensor fusion processor from NVidia Fully AI to reach autonomy Price per unit > $1000 $10 < $1 image sensor builders are therefore increasingly interested in integrating a software layer to their system in order to capture it. Today, image sensors must go beyond taking images they must be able to analyze them. However, to run these types of software, high power computing and memory are necessary, which led to the creation and development of vision processors. The image signal processor (ISP) market offers a steady compound annual growth rate (CAGR) of 6.3%, making the total market worth $4,400M in Meanwhile, the vision processor market is exploding, with a 30.7% CAGR and a market worth $653M in 2017! Today, optimization requires software and hardware to be developed in parallel. Depending on the issues and specifications, companies can invest more in hardware than software or vice versa. However, software is easier to specify, tune and update, and so its growth is more important than hardware. The AI market is therefore expected to reach $35B in 2025 with an estimated CAGR at 50% per year from HARDWARE MARKET AND BUSINESS MODELS Revenues (US$ million) This report carefully evaluates ISP and vision processor market shares and their evolution in order to correctly understand how AI technology impacts the hardware. This market has been divided in two different business models: Intellectual Property (IP) companies, which don t have physical products, and hardware companies, which sell the processors physically. The leaders are pretty easy to identify for each category. ARM and Synopsys lead the IP segment and Omnivision, Mobileye and ON Semiconductor lead the hardware segment. Hardware markets size evolution Intellectual Property - Image signal processor Hardware - Vision processor Intellectual Property - Vision processor Hardware - Image signal processor The AI market, particularly in vision systems, is new and still moving, with hundreds of startups created each year. It has no clear leaders but a lot of highly specialized companies. This report therefore gives a high-level view of driving forces, technology hype, and the most important mergers and acquisitions. The main goal of this report is to understand what is happening with the emergence of AI. Even if it is not a new technology, thanks to technological factors AI has made a spectacular entry into vision systems. It opens new perspectives in various segments such as automotive, surveillance, biometrics and medical. However, it also poses ethical questions, which we have tried to answer. AI technologies promise a bright future in many areas, with rapid software and hardware progress. In autonomous vehicles, AI allows cars to understand the world around them, predict trajectories, communicate and drive. This has led to the development of sensor fusion boards. For example, NVidia s Drive PX boards provide very high performance computing and memory, giving the ability to compile information from many completely different sensors. In surveillance and security, face and iris recognition have never been as powerful, entering the consumer world through the iphone X this year, and behavior recognition is on track. (Yole Développement, November 2017)

18 MARKET & TECHNOLOGY REPORT Al is very exciting for the entire area of vision systems. This report tries to show why it is important to understand the technologies and their impacts, and how to react. AI is in vision systems, from technology to market. Emergence of artificial intelligence - Technological factors $ $ Approximate cost per GFLOPS (10 9 operations per second) IBM 7030 Stretch Amount of data stored (Exabytes bytes) Estimated $ $ $ $100 $1 Beowulf cluster Cray X-MP/48 KASY $0, (Yole Développement, November 2017) TABLE OF CONTENTS (complete content on i-micronews.com) Report objectives and methodology 5 Introduction 12 > Software > Embedded software Image processing 23 > Definition > Basics : Primary ISP architecture for Bayer image sensor > Image processing to recover artefacts > Image processing for color reproduction > Image processing with pre-/postprocessing > Conclusion Artificial intelligence 41 > Definition > A modern history of AI > AI technologies > Discussion Players and market dynamics 59 > ISP and vision processor > AI in vision systems Conclusions 83 Company profile 90 COMPANIES CITED IN THE REPORT (non exhaustive list) Algolux, Amazon, Altek, Ambarella, AMD, ARM, Apple, B-Secur, Biowatch, CEVA, Canon, Chips&Media, Cognitec, Delta ID, Ergoneers, EyeLink, Eyesmart Technology, Eyetech, Face++, Facebook, Fotonation, Google, IBM, Intel, Imagination, Irisys, Iritech, ISCAN, Kairos, LC Technologies, LG, Microsoft, Mobbeel, Mobileye, Movidius, Nec, Nextchip, Nvidia, NXP, Omnivision, ON Semiconductor, OpenCV, Parrot, Pixelworks, Princeton Identity, Qualcomm, SensorMotoric Instruments, Smart Eye, Socionext, Sony, Sthaler, STMicroelectronics, Sunplus, Synopsys, The Eye Tribe, Tobii, Xperi...And more. RELATED REPORTS Benefit from our Bundle & Annual Subscription offers and access our analyses at the best available price and with great advantages 3D TSV and 2.5D Business Update - Market and Technology Trends D Imaging and Sensing 2017 Status of the CMOS Image Sensor Industry 2017 AUTHOR Dr. Yohann Tschudi as a Software & Market Analyst, Dr. Yohann Tschudi is a member of the MEMS & Sensors business unit at Yole Développement (Yole). Yohann is daily working with Yole s analysts to identify, understand and analyze the role of the software parts within any semiconductor products, from the machine code to the highest level of algorithms. Market segments especially analyzed by Yohann include big data analysis algorithms, deep/machine learning, genetic algorithms, all coming from Artificial Intelligence (IA) technologies. After his thesis at CERN (Geneva, Switzerland) in particle physics, Yohann developed a dedicated software for fluid mechanics and thermodynamics applications. Afterwards, he served during 2 years at the University of Miami (FL, United-States) as a research scientist in the radiation oncology department. He was involved in cancer auto-detection and characterization projects using AI methods based on images from Magnetic Resonance Imaging (MRI). During his research career, Yohann has authored and co-authored more than 10 relevant papers. Yohann has a PhD in High Energy Physics and a master degree in Physical Sciences from Claude Bernard University (Lyon, France). Find more details about this report here: Find all our reports on

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29 OUR 2017 REPORTS PLANNING (1/2) MARKET AND TECHNOLOGY REPORTS by Yole Développement o MEMS & SENSORS Acoustic MEMS and Audio Solutions D Imaging & Sensing 2017 Microspectrometers Markets and Applications 2017 Status of the MEMS Industry 2017* MEMS & Sensors for Automotive 2017 High End Inertial Sensors for Defense and Industrial Applications 2017* Magnetic Sensors Market and Technologies 2017 Sensors and Sensing Modules for Smart Homes and Buildings 2017 Sensing and Display for AR/VR/MR 2017 MEMS Packaging 2017 Fingerprint Sensor Applications and Technologies - Consumer Market Focus 2017 o RF DEVICES AND TECHNOLOGIES RF Front End Modules and Components for Cellphones 2017 Advanced RF SiP for Cellphones G and Beyond (Vol 1): Impact on RF Industry, from Infrastructure to Terminals G and Beyond (Vol 2): RF Materials Platform, from Infrastructure to Terminals 2017 RF Technologies for Automotive Applications 2017 GaN and Si LDMOS Market and Technology Trends for RF Power 2017 o MANUFACTURING Glass Substrate Manufacturing 2017 Equipment & Materials for Fan Out Technology 2017 Equipment and Materials for 3D TSV Applications 2017 Emerging Non Volatile Memories 2017* *2016 version still available / **To be confirmed o MEDTECH Status of the Microfluidics Industry 2017 Solid State Medical Imaging 2017 Connected Medical Devices: the Internet of Medical Things 2017 Sensors for Medical Robotics 2017 Artificial Organs: Market & Technology Analysis 2017 Organs-on-a Chip 2017 o ADVANCED PACKAGING Advanced Substrates Overview 2017 Status of the Advanced Packaging Industry 2017 Fan Out Packaging: Market & Technology Trends 2017* 3D Business Update: Market & Technology Trends 2017* Advanced QFN: Market & Technology Trends 2017** Inspection and Metrology for Advanced Packaging Platform 2017** MEMS Packaging 2017 Advanced Packaging for Memories 2017 Advanced RF SiP for Cellphones 2017 Power Packaging Market and Technology Trends 2017 Embedded Die Packaging: Technologies and Markets Trends 2017 o IMAGING & OPTOELECTRONICS 3D Imaging & Sensing 2017 Status of the CMOS Image Sensor Industry 2017* Camera Module for Consumer and Automotive Applications 2017* Uncooled Infrared Imaging Technology & Market Trends 2017* Solid State Medical Imaging 2017 o BATTERY AND ENERGY MANAGEMENT Status of Battery Industry for Stationary, Automotive and Consumer Applications About Yole Développement 25

30 OUR 2017 REPORTS PLANNING (2/2) o POWER ELECTRONICS Status of Power Electronics Industry 2017* Silicon Power Mosfet: Market and Technology Trends 2017 IGBT Market and Technology Trends 2017 Power Packaging Market and Technology Trends 2017 Power SiC 2017: Materials, Devices, and Applications* Power GaN 2017: Materials, Devices, and Applications* Materials Market Opportunities for Cellphone Thermal Management (Battery Cooling, Fast Charging, Data Processing, Battery Cooling, etc.) 2017 Gate Driver Market and Technology Trends 2017 Power Management ICs Market Quarterly Update 2017 Power Electronics for Electrical Aircraft, Rail and Buses 2017** Thermal Management for LED and Power 2017 o COMPOUND SEMICONDUCTORS Power SiC 2017: Materials, Devices, and Applications* Power GaN 2017: Materials, Devices, and Applications* GaN and Si LDMOS Market and Technology Trends for RF Power G and Beyond (Vol 2): RF Materials Platform, from Infrastructure to Terminals 2017 Bulk GaN Substrate Market 2017 o DISPLAYS MicroLED Displays 2017 Display for Augmented Reality, Virtual Reality and Mixed Reality 2017 Quantum Dots for Display Applications 2017 Phosphors & Quantum Dots LED Downconverters for Lighting & Displays Organic Thin Film Transistor 2016: Flexible Displays and Other Applications Automotive Lighting Technology, Industry and Market Trends *2016 version still available / **To be confirmed o SOLID STATE LIGHTING UV LEDs Technology, Manufacturing and Application Trends* Horticultural Lighting Technology, Industry and Market Trends Automotive Lighting Technology, Industry and Market Trends* Active Imaging and Lidar 2017 (Vol 2) - IR Lighting** LED Lighting Module Technology, Industry and Market Trends IR LEDs and Lasers - Technology Applications and Industry Trends 2017 Phosphors & Quantum Dots LED Downconverters for Lighting & Displays MicroLED Displays 2017 CSP LED Lighting Module 2017 LED Packaging 2017 Thermal Management for LED and Power 2017 PATENT ANALYSIS by Knowmade Consumer Physics SCiO Molecular Sensor Patent-to-Product Mapping 2017 Patent Licensing Companies in the Semiconductor Market - Patent Litigation Risk and Potential Targets 2017 MEMS Microphone: Patent Landscape Analysis 2017 MEMS Microphone: Knowles Patent Portfolio Analysis 2017 Microbolometer: Patents Used in Products 2017 Microfluidic Technologies for Diagnostic Applications Patent Landscape 2017 Micropumps: Patent Landscape Analysis 2017 GaN Technology: Top-100 IP Profiles 2017 MicroLEDs: Patent Landscape Analysis 2017 Consumer Physics SCiO Molecular Sensor Patent-to-Product Mapping 2017 Uncooled Infrared Imaging: Patent Landscape Analysis D Monolithic Memory: Patent Landscape Analysis 2017 TEARDOWN & REVERSE COSTING by System Plus Consulting More than 60 teardowns and reverse costing analysis and cost simulation tools to be published in About Yole Développement 26

31 OUR 2016 PUBLISHED REPORTS LIST (1/2) MARKET AND TECHNOLOGY REPORTS by Yole Développement o MEMS & SENSORS Gas Sensors Technology and Market 2016 Status of the MEMs Industry 2016 Sensors for Cellphones and Tablets 2016 Market and Technology Trends of Inkjet Printheads 2016 Sensors for Biometry and Recognition 2016 Silicon Photonics 2016 o IMAGING & OPTOELECTRONICS Status of the CMOS Image Sensor Industry 2016 Uncooled Infrared Imaging Technology & Market Trends 2016 Imaging Technologies for Automotive 2016 Sensors for Drones & Robots: Market Opportunities and Technology Evolution 2016 o MEDTECH BioMEMS 2016 Point of Care Testing 2016: Application of Microfluidic Technologies o ADVANCED PACKAGING Embedded Die Packaging: Technology and Market Trends D & 3D IC TSV Interconnect for Advanced Packaging: Business Update 2016 Fan-Out: Technologies and Market Trends 2016 Fan-In Packaging: Business update 2016 Status and Prospects for the Advanced Packaging Industry in China 2016 o MANUFACTURING Thin Wafer Processing and Dicing Equipment Market 2016 Emerging Non Volatile Memories 2016 o COMPOUND SEMICONDUCTORS Power GaN 2016: Epitaxy and Devices, Applications and Technology Trends GaN RF Devices Market: Applications, Players, Technology and substrates 2016 Sapphire Applications & Market 2016: from LED to Consumer Electronics Power SiC 2016: Materials, Devices, Modules, and Applications o LED UV LED Technology, Manufacturing and Applications Trends 2016 OLED for Lighting 2016 Technology, Industry and Market Trends Automotive Lighting: Technology, Industry and Market Trends 2016 Thermal Management Technology and Market Perspectives in Power Electronics and LEDs 2017 Organic Thin Film Transistor 2016: Flexible Displays and Other Applications Sapphire Applications & Market 2016: from LED to Consumer Electronics LED Packaging 2017: Market, Technology and Industry Landscape o POWER ELECTRONICS Power Electronics for EV/HEV 2016: Market, Innovations and Trends Status of Power Electronics Industry 2016 Passive Components Technologies and Market Trends for Power Electronics 2016 Power SiC 2016: Materials, Devices, Modules, and Applications Power GaN 2016: Epitaxy and Devices, Applications, and Technology Trends Inverter Technologies Trends & Market Expectations 2016 Opportunities for Power Electronics in Renewable Electricity Generation 2016 Thermal Management Technology and Market Perspectives in Power Electronics and LEDs 2017 GaN RF Devices Market: Applications, Players, Technology and substrates 2016 o BATTERY AND ENERGY MANAGEMENT Beyond Li-ion Batteries: Present and Future Li-ion Technology Challengers 2016 Stationary Storage and Automotive Li-ion Battery Packs 2016 Opportunities for Power Electronics in Renewable Electricity Generation About Yole Développement 27

32 OUR 2016 PUBLISHED REPORTS LIST (2/2) PATENT ANALYSIS by Knowmade Microbattery Patent Landscape Analysis Miniaturized Gas Sensors Patent Landscape Analysis 3D Cell Culture Technologies Patent Landscape Phosphors and QDs for LED Applications Patent Landscape TSV Stacked Memory Patent Landscape Fan-Out Wafer Level Packaging Patent Landscape Analysis TEARDOWN & REVERSE COSTING by System Plus Consulting More than 45 teardowns and reverse costing analysis and cost simulation tools published in MORE INFORMATION o o All the published reports from the Yole Group of Companies are available on our website Ask for our Bundle and Annual Subscription offers: With our bundle offer, you choose the number of reports you are interested in and select the related offer. You then have up to 12 months to select the required reports from the Yole Développement, System Plus Consulting and KnowMade offering. Pay once and receive the reports automatically (multi-user format). Contact your sales team according to your location (see the last slide) About Yole Développement 28

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34 CONTACT INFORMATION o o CONSULTING AND SPECIFIC ANALYSIS North America: Steve LaFerriere, Director of Northern America Business Development laferriere@yole.fr Japan & Rest of Asia: Takashi Onozawa, General Manager, Asia Business Development onozawa@yole.fr Greater China: Mavis Wang, Director of Greater China Business Development wang@yole.fr RoW: Jean-Christophe Eloy, CEO & President, Yole Développement eloy@yole.fr REPORT BUSINESS North America: Steve LaFerriere, Director of Northern America Business Development laferriere@yole.fr Europe: Lizzie Levenez, EMEA Business Development Manager levenez@yole.fr Rest of Asia: Takashi Onozawa, General Manager, Asia Business Development onozawa@yole.fr Japan & Asia: Miho Othake, Account Manager ohtake@yole.fr Greater China: Mavis Wang, Director of Greater China Business Development wang@yole.fr Follow us on o FINANCIAL SERVICES Jean-Christophe Eloy, CEO & President eloy@yole.fr o GENERAL Public Relations: leroy@yole.fr info@yole.fr About Yole Développement 30