The document provides an in-depth analysis of the image signal processor and vision processor market including market forecasts, trends, technologies, and ecosystems from 2019-2024. It examines the IP and silicon markets, applications in mobile and automotive, and provides forecasts for unit shipments and revenues for ISPs and VPs by application. Key trends discussed include the growth of AI and computer vision algorithms in mobile and automotive vision systems.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/03/market-analysis-on-socs-for-imaging-vision-and-deep-learning-in-automotive-and-mobile-markets-a-presentation-from-yole-developpement/
For more information about edge AI and vision, please visit:
http://www.edge-ai-vision.com
John Lorenz, Market and Technology Analyst for Computing and Software at Yole Développement, delivers the presentation “Market Analysis on SoCs for Imaging, Vision and Deep Learning in Automotive and Mobile Markets” at the Edge AI and Vision Alliance’s March 2020 Vision Industry and Technology Forum. Lorenz presents Yole Développement’s latest analysis on the evolution of SoCs for imaging, vision and deep learning.
For the first time in its history, the automotive industry must face new industrial and technological
challenges while undergoing dramatic changes in its value chain.
More information: https://www.i-micronews.com/products/automotive-semiconductor-trends-2021/
Definition
Embedded systems vs. General Computing Systems
Core of the Embedded System
Memory
Sensors and Actuators
Communication Interface
Embedded Firmware
Other System Components
PCB and Passive Components
Radar Technologies For Automotive 2018 report by Yole Développement Yole Developpement
How will radar sensor technology shape the cars of the future? Prepare for the automotive sensor industry’s golden age, in which radar will be increasingly viewed as a key technology for autonomous vehicles.
AUTOMOTIVE IS EXPERIENCING AN EXPLOSION OF NEW HIGH-TECH APPLICATIONS
Automatic emergency braking, adaptive cruise control, and lane-change assist are some examples of these new applications. Spurred by the New Car Assessment Program, OEMs are designing cars with numerous sensors that enable applications like these. And since most of these new applications are safety-related, the sensors must be highly accurate. This means very tight specifications for object detection and classification, as well as being ultra-reliable: operable in every weather condition, in poor lighting, near or far, and with a wide field of view. Radar technology is well-suited to fulfill most of these requirements. We say “most” because object classification is not currently possible with radar, but certain companies are moving quickly to unlock this capability in imaging radar.
Radar has an impressive technology roadmap allowing for huge resolution improvement as well as device miniaturization and cost reduction. Despite small growth (~3%) in global car sales until 2022, Yole Développement expects an average growth rate of 25% for radar module sales, and an average growth rate of 22% for radar chip sales over the next five years - with autonomous driving being the next long-term driver for radar technology growth.
More information on that report at http://www.i-micronews.com/reports.html
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/03/market-analysis-on-socs-for-imaging-vision-and-deep-learning-in-automotive-and-mobile-markets-a-presentation-from-yole-developpement/
For more information about edge AI and vision, please visit:
http://www.edge-ai-vision.com
John Lorenz, Market and Technology Analyst for Computing and Software at Yole Développement, delivers the presentation “Market Analysis on SoCs for Imaging, Vision and Deep Learning in Automotive and Mobile Markets” at the Edge AI and Vision Alliance’s March 2020 Vision Industry and Technology Forum. Lorenz presents Yole Développement’s latest analysis on the evolution of SoCs for imaging, vision and deep learning.
For the first time in its history, the automotive industry must face new industrial and technological
challenges while undergoing dramatic changes in its value chain.
More information: https://www.i-micronews.com/products/automotive-semiconductor-trends-2021/
Definition
Embedded systems vs. General Computing Systems
Core of the Embedded System
Memory
Sensors and Actuators
Communication Interface
Embedded Firmware
Other System Components
PCB and Passive Components
Radar Technologies For Automotive 2018 report by Yole Développement Yole Developpement
How will radar sensor technology shape the cars of the future? Prepare for the automotive sensor industry’s golden age, in which radar will be increasingly viewed as a key technology for autonomous vehicles.
AUTOMOTIVE IS EXPERIENCING AN EXPLOSION OF NEW HIGH-TECH APPLICATIONS
Automatic emergency braking, adaptive cruise control, and lane-change assist are some examples of these new applications. Spurred by the New Car Assessment Program, OEMs are designing cars with numerous sensors that enable applications like these. And since most of these new applications are safety-related, the sensors must be highly accurate. This means very tight specifications for object detection and classification, as well as being ultra-reliable: operable in every weather condition, in poor lighting, near or far, and with a wide field of view. Radar technology is well-suited to fulfill most of these requirements. We say “most” because object classification is not currently possible with radar, but certain companies are moving quickly to unlock this capability in imaging radar.
Radar has an impressive technology roadmap allowing for huge resolution improvement as well as device miniaturization and cost reduction. Despite small growth (~3%) in global car sales until 2022, Yole Développement expects an average growth rate of 25% for radar module sales, and an average growth rate of 22% for radar chip sales over the next five years - with autonomous driving being the next long-term driver for radar technology growth.
More information on that report at http://www.i-micronews.com/reports.html
Imaging Technologies for Automotive 2016 Report by Yole Developpement Yole Developpement
Imaging technology, which is currently mainly cameras, is exploding into the automotive space, and is set to grow at 20% CAGR to reach $7.3B in 2021
INFOTAINMENT AND ADVANCED DRIVER ASSISTANCE SYSTEMS (ADAS) PROPEL AUTOMOTIVE IMAGING
Since 2008, when a recession acted as a wakeup call to the whole industry, the automotive market has undergone obvious structural change. Capitalizing on technologies initially developed for smartphones, electronics have invaded, and imaging technology is now taking center stage. From less than one camera per car on average in 2015, there will be more than three cameras per car by 2021, which means 371 million automotive imaging devices.
Cameras were initially mounted for ADAS purposes on high-end vehicles, with deep learning image analysis techniques promoting early adoption. The Israeli company Mobileye has been instrumental in bringing this technology to market, along with On Semiconductor, which provided the CMOS image sensor. Copycat competition will probably pick up as the market now justifies initial investment in design and technology. It is now a well-established fact that vision-based autonomous emergency braking (AEB) is possible and saves life. Adoption of forward ADAS cameras will therefore accelerate.
Growth of imaging for automotive is also being fueled by the park assist application, and 360° surround view camera volume is skyrocketing. While it’s becoming mandatory in the US to have a rearview camera, that uptake is dwarfed by 360° surround view cameras, which enable a “bird’s eye view” perspective. This trend is most beneficial to companies like Omnivision at sensor level and Panasonic and Valeo, which have become the main manufacturers of automotive cameras.
Mirror replacement cameras are currently the big unknown and take-off will primarily depend on its appeal and car design regulation. Europe and Japan are at the forefront of this trend, which should become slightly significant by 2021.
Solid state lidar is well talked about and will start to be found in high end cars by 2021. Cost reduction will be a key driver as the push for semi-autonomous driving will be felt more strongly by car manufacturers. The report will analyse the impact of lidar for automotive vision in detail.
Night vision cameras using Long Wave Infrared (LWIR) technology were initially perceived as a status symbol. However, they’re increasingly appreciated for their ability to automatically detect pedestrians and wildlife. LWIR will therefore become integrated into ADAS systems in future.
3D cameras will be limited to in-cabin infotainment and driver monitoring. This technology will be key for luxury cars and therefore is of limited use today.
If any significant semi-autonomous trend picks up, the technology will probably become mandatory, due to safety issues.
More information on that report at http://www.i-micronews.com/reports.html
2015 D-STOP Symposium session by Ram Mirwani of AWR/National Instruments.
Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/
LiDAR for Automotive and Industrial Applications 2019 by Yole DéveloppementYole Developpement
Is rationalization happening in the LiDAR market?
More information on: https://www.i-micronews.com/produit/lidar-for-automotive-and-industrial-applications-2019/
Mike McBride will provide a look at the Industrial IoT (IIoT) landscape and the OT/IT convergence. He will cover several use cases including healthcare, entertainment and smart buildings. He will cover the challenges IIoT networking faces with emerging technologies and how edge computing will provide increased performance, security and reliability. Mike will discuss the various Edge Computing standards & opensource forums along with proposed architectures. And Mike will present new solutions being proposed (ICN, slicing, Blockchain) to support the bandwidth, latency and security requirements within Industrial verticals.
About the speaker: As Sr. Director of Innovation & Strategy, within Huawei's IP Network BU, Mike leads Industrial IoT, Edge Computing and IP/SDN architecture, standardization, and strategy across product lines and industry forums. He leads architecture and standardization activities within the IIc and BBF and has served as an IETF Working Group chair for 15 years. Mike has led emerging technology projects within opensource communities and played a key role in the formation of OPEN-O (Now ONAP). He is an Ericsson alum where he developed and directed SDN/NFV network architectures. And for many years with Cisco, Mike supported customers, worked in development teams and managed mobility, wireless and video projects across BUs. Mike began his career supporting customers at Apple Computer. He resides in Orange County, CA
Proliferation of cameras for imaging and sensing is driving CMOS image sensor (CIS) growth.
More information on that report at : https://www.i-micronews.com/report/product/status-of-the-cis-industry-2018.html
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/xilinx/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nick Ni, Director of Product Marketing at Xilinx, presents the "Xilinx AI Engine: High Performance with Future-proof Architecture Adaptability" tutorial at the May 2019 Embedded Vision Summit.
AI inference demands orders- of-magnitude more compute capacity than what today’s SoCs offer. At the same time, neural network topologies are changing too quickly to be addressed by ASICs that take years to go from architecture to production. In this talk, Ni introduces the Xilinx AI Engine, which complements the dynamically- programmable FPGA fabric to enable ASIC-like performance via custom data flows and a flexible memory hierarchy. This combination provides an orders-of-magnitude boost in AI performance along with the hardware architecture flexibility needed to quickly adapt to rapidly evolving neural network topologies.
Industry 4.0 (IIoT) is changing the face of manufacturing like never before. Global situation of tariff war and re-shoring are catalysts driving this change in addition to technology adaption.
Check out this Comparative Analysis between Industry 4.0 (IIoT) and SCADA systems to understand the basic differences between the two.
[Webinar Presentation] Best Practices for IT/OT ConvergenceSchneider Electric
All over the world, utilities are facing up to the task of integrating information technology (IT) operations with those of operational technology (OT). What's driving it? How can utilities prepare? What should they expect?
The webinar recording is also available on-demand. To view it, please click here: http://goo.gl/b3kxm5
Rapid strides in Technology are making mobility seamless. Consumers are connected to the external world through a plethora of personal smart devices.
Automotive sector too is witnessing an unprecedented absorption of these technologies en masse to provide a connected car experience. There is an immediate need for the right mix of technologies/processes and the right delivery mechanisms for providing the Car occupants safer & ultimate driving Experience while generating value for the stake holders.
This presentation will cover some of the key technology trends and challenges involved in realizing the connected car functions
It is a presentation for the Embedded System Basics. It will be very useful for the engineering students who need to know the basics of Embedded System.
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsAnusha Aravindan
AIoT(Artificial intelligence of things) is a relatively new term and has recently become a hot topic which combines two of the hottest acronyms AI( Artificial intelligence) and Internet of things (IoT)
Market will more than double by 2025 driven by heavy investments in data centers.
More information: https://www.i-micronews.com/products/optical-transceivers-for-datacom-telecom-2020/
Machine Vision for Industry and Automation 2018 Report by Yole DeveloppementYole Developpement
Machine vision is at the heart of the automation revolution.
More information on that report at https://www.i-micronews.com/report/product/machine-vision-for-industry-and-automation-2018.html
Computing and AI technologies for mobile and consumer applications 2021 - SampleYole Developpement
Penetrating everyday products will see the market for AI technologies for the consumer market reach $5.6B in 2026.
More information : https://www.i-micronews.com/products/computing-and-ai-technologies-for-mobile-and-consumer-applications-2021/
Imaging Technologies for Automotive 2016 Report by Yole Developpement Yole Developpement
Imaging technology, which is currently mainly cameras, is exploding into the automotive space, and is set to grow at 20% CAGR to reach $7.3B in 2021
INFOTAINMENT AND ADVANCED DRIVER ASSISTANCE SYSTEMS (ADAS) PROPEL AUTOMOTIVE IMAGING
Since 2008, when a recession acted as a wakeup call to the whole industry, the automotive market has undergone obvious structural change. Capitalizing on technologies initially developed for smartphones, electronics have invaded, and imaging technology is now taking center stage. From less than one camera per car on average in 2015, there will be more than three cameras per car by 2021, which means 371 million automotive imaging devices.
Cameras were initially mounted for ADAS purposes on high-end vehicles, with deep learning image analysis techniques promoting early adoption. The Israeli company Mobileye has been instrumental in bringing this technology to market, along with On Semiconductor, which provided the CMOS image sensor. Copycat competition will probably pick up as the market now justifies initial investment in design and technology. It is now a well-established fact that vision-based autonomous emergency braking (AEB) is possible and saves life. Adoption of forward ADAS cameras will therefore accelerate.
Growth of imaging for automotive is also being fueled by the park assist application, and 360° surround view camera volume is skyrocketing. While it’s becoming mandatory in the US to have a rearview camera, that uptake is dwarfed by 360° surround view cameras, which enable a “bird’s eye view” perspective. This trend is most beneficial to companies like Omnivision at sensor level and Panasonic and Valeo, which have become the main manufacturers of automotive cameras.
Mirror replacement cameras are currently the big unknown and take-off will primarily depend on its appeal and car design regulation. Europe and Japan are at the forefront of this trend, which should become slightly significant by 2021.
Solid state lidar is well talked about and will start to be found in high end cars by 2021. Cost reduction will be a key driver as the push for semi-autonomous driving will be felt more strongly by car manufacturers. The report will analyse the impact of lidar for automotive vision in detail.
Night vision cameras using Long Wave Infrared (LWIR) technology were initially perceived as a status symbol. However, they’re increasingly appreciated for their ability to automatically detect pedestrians and wildlife. LWIR will therefore become integrated into ADAS systems in future.
3D cameras will be limited to in-cabin infotainment and driver monitoring. This technology will be key for luxury cars and therefore is of limited use today.
If any significant semi-autonomous trend picks up, the technology will probably become mandatory, due to safety issues.
More information on that report at http://www.i-micronews.com/reports.html
2015 D-STOP Symposium session by Ram Mirwani of AWR/National Instruments.
Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/
LiDAR for Automotive and Industrial Applications 2019 by Yole DéveloppementYole Developpement
Is rationalization happening in the LiDAR market?
More information on: https://www.i-micronews.com/produit/lidar-for-automotive-and-industrial-applications-2019/
Mike McBride will provide a look at the Industrial IoT (IIoT) landscape and the OT/IT convergence. He will cover several use cases including healthcare, entertainment and smart buildings. He will cover the challenges IIoT networking faces with emerging technologies and how edge computing will provide increased performance, security and reliability. Mike will discuss the various Edge Computing standards & opensource forums along with proposed architectures. And Mike will present new solutions being proposed (ICN, slicing, Blockchain) to support the bandwidth, latency and security requirements within Industrial verticals.
About the speaker: As Sr. Director of Innovation & Strategy, within Huawei's IP Network BU, Mike leads Industrial IoT, Edge Computing and IP/SDN architecture, standardization, and strategy across product lines and industry forums. He leads architecture and standardization activities within the IIc and BBF and has served as an IETF Working Group chair for 15 years. Mike has led emerging technology projects within opensource communities and played a key role in the formation of OPEN-O (Now ONAP). He is an Ericsson alum where he developed and directed SDN/NFV network architectures. And for many years with Cisco, Mike supported customers, worked in development teams and managed mobility, wireless and video projects across BUs. Mike began his career supporting customers at Apple Computer. He resides in Orange County, CA
Proliferation of cameras for imaging and sensing is driving CMOS image sensor (CIS) growth.
More information on that report at : https://www.i-micronews.com/report/product/status-of-the-cis-industry-2018.html
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/xilinx/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nick Ni, Director of Product Marketing at Xilinx, presents the "Xilinx AI Engine: High Performance with Future-proof Architecture Adaptability" tutorial at the May 2019 Embedded Vision Summit.
AI inference demands orders- of-magnitude more compute capacity than what today’s SoCs offer. At the same time, neural network topologies are changing too quickly to be addressed by ASICs that take years to go from architecture to production. In this talk, Ni introduces the Xilinx AI Engine, which complements the dynamically- programmable FPGA fabric to enable ASIC-like performance via custom data flows and a flexible memory hierarchy. This combination provides an orders-of-magnitude boost in AI performance along with the hardware architecture flexibility needed to quickly adapt to rapidly evolving neural network topologies.
Industry 4.0 (IIoT) is changing the face of manufacturing like never before. Global situation of tariff war and re-shoring are catalysts driving this change in addition to technology adaption.
Check out this Comparative Analysis between Industry 4.0 (IIoT) and SCADA systems to understand the basic differences between the two.
[Webinar Presentation] Best Practices for IT/OT ConvergenceSchneider Electric
All over the world, utilities are facing up to the task of integrating information technology (IT) operations with those of operational technology (OT). What's driving it? How can utilities prepare? What should they expect?
The webinar recording is also available on-demand. To view it, please click here: http://goo.gl/b3kxm5
Rapid strides in Technology are making mobility seamless. Consumers are connected to the external world through a plethora of personal smart devices.
Automotive sector too is witnessing an unprecedented absorption of these technologies en masse to provide a connected car experience. There is an immediate need for the right mix of technologies/processes and the right delivery mechanisms for providing the Car occupants safer & ultimate driving Experience while generating value for the stake holders.
This presentation will cover some of the key technology trends and challenges involved in realizing the connected car functions
It is a presentation for the Embedded System Basics. It will be very useful for the engineering students who need to know the basics of Embedded System.
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsAnusha Aravindan
AIoT(Artificial intelligence of things) is a relatively new term and has recently become a hot topic which combines two of the hottest acronyms AI( Artificial intelligence) and Internet of things (IoT)
Market will more than double by 2025 driven by heavy investments in data centers.
More information: https://www.i-micronews.com/products/optical-transceivers-for-datacom-telecom-2020/
Machine Vision for Industry and Automation 2018 Report by Yole DeveloppementYole Developpement
Machine vision is at the heart of the automation revolution.
More information on that report at https://www.i-micronews.com/report/product/machine-vision-for-industry-and-automation-2018.html
Computing and AI technologies for mobile and consumer applications 2021 - SampleYole Developpement
Penetrating everyday products will see the market for AI technologies for the consumer market reach $5.6B in 2026.
More information : https://www.i-micronews.com/products/computing-and-ai-technologies-for-mobile-and-consumer-applications-2021/
How Schneider Electric Assures Its Salesforce Lightning Migration with Thousa...ThousandEyes
ThousandEyes webinar from Tuesday September 17th 2019, presented by Archana Kesavan, Director of Product Marketing at ThousandEyes and Anil Sistal, Platform Architect at Schneider Electric on the topic of Schneider Electrics migration to ThousandEyes Synthetic Monitoring for Salesforce Lightning.
Status of the CMOS Image Sensor Industry 2016: New Dynamics in Market and Tec...Yole Developpement
New functions are pushing change in CMOS image sensors, boosting the market toward $18.8B in 2021 at 10.4% CAGR
Beyond $10B: The CMOS image sensor industry keeps growing at high pace
Driven by renewed mobile and automotive applications, the CMOS image sensor (CIS) industry is expected to expand at a compound annual growth rate (CAGR) of 10.4% from 2015 to 2021, reaching US$18.8B market value by 2021.
Yole Développement expects sustained growth of the CMOS image sensor industry for the next five years. Increasing camera content in smartphones will more than offset slower smartphone volume growth. The trend for dual and 3D cameras will have a major impact on CIS volumes. While it is too early to fully describe the strategy of the main actors yet, some products are already on the market. The 2016 report comprehensively covers key market and technology choices.
One big story this year is the consumer market, which is recovering from the total collapse of digital photography. While action cameras seem to have reached a ceiling, new applications such as drones, robots, virtual reality and augmented reality are ready to rejuvenate this emblematic market. The Automotive camera market has established itself as a key growth market for CIS. The Advanced Driver Assistance (ADAS) trend is further increasing pressure on vendors to provide sensors beyond their current technical capabilities. Image analysis is the new frontier and early usage of artificial intelligence is catching people’s imagination. We are therefore in the middle of an explosive growth pattern that will not slow down before 2021. An exceptionally high 23% CAGR is predicted in automotive for the 2015-2021 period.
Industrial, consumer, and automotive applications are driving the adoption of neuromorphic computing and sensing technologies. The first products are now hitting the market.
More information: https://www.i-micronews.com/products/neuromorphic-computing-and-sensing-2021/
For the full video of this presentation, please visit:
https://www.embedded-vision.com/industry-analysis/video-interviews-demos/2d-and-3d-sensing-markets-applications-and-technologies-pre
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Guillaume Girardin, Photonics, Sensing and Display Division Director at Yole Développement, delivers the presentation "2D and 3D Sensing: Markets, Applications, and Technologies" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Girardin details the optical depth sensor market and application trends.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-tschudi
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Yohann Tschudi, Technology and Market Analyst at Yole Développement, presents the "AI Is Moving to the Edge—What’s the Impact on the Semiconductor Industry?" tutorial at the May 2019 Embedded Vision Summit.
Artificial intelligence is proliferating into numerous edge applications and disrupting numerous industries. Clearly this represents a huge opportunity for technology suppliers. But it can be difficult to discern exactly what form this opportunity will take. For example, will edge devices perform AI computation locally, or in the cloud? Will edge devices use separate chips for AI, or will AI processing engines be incorporated into the main processor SoCs already used in these devices?
In this talk, Tschudi answers these questions by presenting and explaining his firm's market data and forecasts for AI processors in mobile phones, drones, smart home devices and personal robots. He explains why there is a strong trend towards executing AI computation at the edge, and quantifies the opportunity for separate processor chips and on-chip accelerators that address visual and audio AI tasks.
Paramount to the future of safety and autonomy, the automotive imaging market is at a key crossroads.
More information on https://www.i-micronews.com/products/imaging-for-automotive-2019/
CMOS Image Sensor Service – Imaging Research 2019 Monitors by Yole DéveloppementYole Developpement
As camera quantity and die size increase per end-device, a 10.1% year-on-year growth rate is expected for 2019.
More information on CMOS monitor at: https://www.i-micronews.com/products/cmos-image-sensor-service-camera-module-research/
Artificial Intelligence Computing for Automotive 2019 Report by Yole Développ...Yole Developpement
Artificial Intelligence for automotive: why you should care.
More information on that report at: https://www.i-micronews.com/produit/artificial-intelligence-computing-for-automotive-2019/
Status of the Camera Module Industry 2019 – Focus on Wafer Level Optics repor...Yole Developpement
CMOS Camera Modules (CCM) have become a key sensor technology – what are the dynamics and strategies in this highly competitive market?
More information on: https://www.i-micronews.com/produit/status-of-the-camera-module-industry-2019-focus-on-wafer-level-optics/
How will AI impact the semiconductor market through consumer applications?
More information on that report at : https://www.i-micronews.com/report/product/hardware-and-software-for-ai-2018-consumer-focus.html
The one million robotic vehicle milestone will be reached by end of the decade: The industrial phase has been launched.
More information on: https://www.i-micronews.com/products/sensors-for-robotic-mobility-2020/
Status of the CMOS Image Sensor Industry 2017 - Report by Yole DeveloppementYole Developpement
New applications are transforming the market and technology playing field for CMOS image sensors
It’s ten years down the line from the initial Apple iPhone that started the smartphone era. Since then, CMOS imaging has benefited from huge market demand and a technology-driven environment, resulting in an $11.6B industry in 2016. Photography and video is the main application, which is totally transformed by new use cases, new devices and new technologies.
The mobile market is key for the CMOS image sensor (CIS) industry. Despite saturation in the number of handsets, the CIS market has been able to maintain a 10.5% compound annual growth rate (CAGR) for the 2016-2022 period due to the introduction of dual and 3D cameras. These additional cameras are changing the industry’s drivers from form factor and image quality to interactivity.
Penetration into higher added value markets such as automotive, security and medical shows that CIS products are transforming use cases across the board. CIS technology adoption allows greater automation levels at low cost, while using newly available computing architectures such as deep learning. The CMOS image sensor industry is currently in a virtuous circle where a new technology is providing true customer value.
Sensors and Data Management for Autonomous Vehicles report 2015 by Yole Devel...Yole Developpement
Multiple sensing technologies will ensure many market opportunities for Tier 1 players, Tier 2 players, and newcomers alike
Sensor technologies are a driving force in making fully autonomous vehicles a reality. Automakers are racing to develop safe self-driving cars, but this race is a distance run more than a sprint, where multiple automation stages will imply multiple sensors. Ultrasonic sensors, radars, and multiple cameras systems are already embedded in high-end vehicles -- and within 10 years, they could also include long-range cameras, LIDAR, micro bolometer and accurate dead reckoning. These devices will work concurrently and each technology will support another to ensure codependency and avoid concerns. Even though sensors are only part of the puzzle, their market opportunities are promising.
Artificial Intelligence Computing for Consumer 2019 report by Yole Développem...Yole Developpement
While AI is a feature expected in smartphones, this fantastic technology has spread like wildfire to the smart home ecosystem and is profoundly impacting the semiconductor industry.
More information on https://www.i-micronews.com/products/artificial-intelligence-computing-for-consumer-2019/
Camera Module Industry 2017 Report by Yole Developpement Yole Developpement
New technologies and applications have restructured the Compact Camera Module industry
AT 12.2% CAGR FOR THE NEXT FIVE YEARS, THE COMPACT CAMERA MODULE INDUSTRY (CCM) IS A GROWTH POWERHOUSE WITH NUMEROUS LARGE COMPANIES THRIVING IN A DYNAMIC MARKET
In 2015, Yole Développement published its first report on the camera module industry and mentioned the immaturity of the ecosystem with numerous small players especially for module assembly. Now the dust has settled and giant camera module players have emerged such as LG Innotek, Semco, Foxconn Sharp, O-Film and Sunny Optical. This 2017 edition is giving you the insights into the trajectory of the industry and of more than 30 players serving mobile and other applications such as automotive and security.
Historically one could differentiate the faith of camera module market from the sub parts such as the image sensor, the lens and the autofocus or optical image stabilization system (Voice Coil Motors - VCM). It seems that differentiated growth has now ended and every sub segment is enjoying almost equal benefit from the rising market tide. This convergence is in part due to the end of quasi-monopoly from Sony in the image sensor sub-segment now joined by Samsung and Omnivision. The story is very similar for Largan Precision in the lens set sub-segment which is now facing renewed competition from Sunny Optical, Kantatsu and Genious Optical.
The last sub-domain of our interest in this report is VCMs. The growth of VCM companies has been undercut by dire structuration efforts. We had mentioned the inability of the VCM to serve the demand in the mobile market. Price pressures have changed the face of competition with competitors such as Mitsumi and Shicoh which were forced out and new players such as New Shicoh and Jawha to take center stage.
More information on that report at http://www.i-micronews.com/reports.html
Microphones, Microspeakers and Audio Solutions Market and Technology Trends 2019Yole Developpement
The voice-based world is shaking up the audio industry, making it worth $20.8B in 2024.
More information on https://www.i-micronews.com/products/microphones-microspeakers-and-audio-solutions-market-and-technology-trends-2019/
Similar to Image Signal Processor and Vision Processor Market and Technology Trends 2019 report by Yole Développement (20)
For the first time, the processor monitor is including FPGA, CPU, GPU, and APU including all the IDMs, fabless companies, and foundries in the business.
More information : https://www.i-micronews.com/products/application-processor-quarterly-market-monitor/
MicroLED Displays - Market, Industry and Technology Trends 2021Yole Developpement
Strong momentum for MicroLED with progress on all fronts. Cost is the biggest challenge, but Apple and Samsung are carving paths toward the consumer.
More information; https://www.i-micronews.com/products/microled-displays-market-industry-and-technology-trends-2021/
System-in-Package Technology and Market Trends 2021 - SampleYole Developpement
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Major cyber events in 2024
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6. 6
REPORT SCOPE
Define
technologies,
markets size,
market
revenues,
players and
ecosystems
Image processing and image analysis
Hardware
Business
Image Signal Processor
Vision Processor
Intellectual Property
Silicon
Which hardware?
Which applications?
Focus on
Applications
Mobile Automotive
Which type of business
model?
16. 16
IMAGE SIGNAL PROCESSOR – WHICH APPLICATIONS?
Following CMOS image sensor volume shipments
• Image Signal Processors are
every where : for each sensor,
at least one ISP is needed to
process the data;
• Image signal processor
market is logically related to
the CMOS image sensor
market:
• In terms of applications ,
smartphones are far ahead
with more than 6 billions
image sensors expected in
2023;
• This makes sense as cameras
are multiplied in
smartphones.
• In general, the market is and
will always grow as more
applications include cameras
(virtual assistant, AR/VR,
drones, ADAS cameras, in-
cockpit cameras)
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
2012 2013 2014 2015 2016 2017 2018e 2019e 2020e 2021e 2022e 2023e
VolumeinMunits
Smartphones Rear Smartphones Front Feature phones Rear Feature phones Front
DSLR / ILCs DSC Video camcorder Action Cam
Drones Personal Robotics Game Stations AR-VR Headset
Home IoT devices Smart watch Computer mice Tablet
Laptop PC In-cabin Infotainment Front ADAS
Capsule endoscopy X-Ray Dental IO X-Ray Flat Panel PET SiPM
Surveillance Cameras Barcode readers Machine vision Defense
Professional & broadcast cameras TOTAL
2012 - 2023 CIS volume shipment (Munits)
Per application
17. 17
VISION PROCESSOR – WHICH SEGMENT?
Pushed by Artificial Intelligence
Need of
power to run
AI algorithms
in more and
more
applications
3 markets are pushing to get more and more AI
Biometry
Photography
Computation of AI algorithms done with a
combination of (embedded)VPU+CPU+GPU
(+NPU)
Mobile
Automotive
Presence of either a discreteVP for
computingAI algorithms using output from
cameras or fusion platform
Autonomous
driving
Surveillance
If computing on the edge, presence of a
discreteVP for computingAI algorithms
using output from the camera
Smart home
Biometry
Infotainment
Not analyzed in
detail in this
report
18. 18
• Two kinds of actors:
• OEMs: applications preinstalled on phones;
• Software companies: focused on downloadable,AI-enhanced phone applications.
• Applications:
• Biometric authentication: face recognition, voice recognition, iris recognition;
• Object and scene analysis:object recognition, scene recognition;
• Augmented reality:adding information to reality;
• User-experience optimisation:learning from habits;
• Natural language processing: speech recognition and natural language understanding;
ARTIFICIAL INTELLIGENCE IN SMARTPHONES
Segmentation
AI in
smartphones
is centered
mostly on
imaging
Imaging
Apple Face ID
Apps using AI algorithms: Hugging
face, Snapchat, Facebook
23. 23
GOAL OF THE CHAPTER
Volumes, penetration rate and average selling prices assumptions
Contour of the
study
Assumption on
the type of
processing/
computing
hardware
Assumption on
the business
model
• Volume have been determined using CMOS Image Sensor
shipments per applications;
• Determination of the penetration rate and average selling price
have been determined based on teardowns when they exist or
comparison with existing applications that have the same type
of processing or computing hardware;
• Prices have been based on teardowns and comparison of
teardowns from System Plus Consulting.
Market Product/System Assumptions
Consumer Action Cam
ISP used for particular rendering,
stabilization and quality. Penetration
rate of VP increasing using some
heavy computer vision algorithms
and even AI
Consumer Video camcorder Classical ISP
Consumer Drones
Drones with camera use VP with an
ISP embedded in it
Consumer DSLR / ILCs
Mid End ISP for high perf
photography and high number of
pixels and then embedded in VP
Consumer DSC Classical ISP
Consumer Personal Robotics Same as drones
Consumer Game Stations
ISP standalone and then ISP
embedded in VP
Consumer AR-VR Headset
ISP and VP embedded in AP. Same as
smartphones
Consumer Personal Assistant
ISP standalone and then ISP
embedded in VP
Consumer Smart cameras Same as personal assistant
Consumer Wrist worn
ISP and VP embedded in AP. Same as
smartphones
Market Product/System Assumptions
Computing Tablet
ISP and VP embedded in AP. Same as
smartphones
Computing Laptop Stand alone ISP
Medical Endoscopy Stand alone ISP
Industrial
Surveillance
Cameras
High End ISP for heavy computer
vision algorithms and when not
embedded in VP
Industrial Machine vision VP only
Definitions
ISP: Image Signal Processor
VP: Vision Processor
AP: Application Processor
IP: Intellectual Property business model
• In the case where ISP and/or VP is embedded in the AP, ASP corresponds to the
price of the surface of silicium plus a fixed price corresponding to the type of
functionalities;
• If ISP in embedded in the VP, only the VP is taken in account because the VP runs by
itself and if necessary the image processing pipeline.
26. The report focuses on describing the markets
related to hardware needed for image processing.
Behind a camera, there may be several ways to
process raw data depending on the purpose.
The alternatives usually break down into
viewing or analyzing the image to understand
the environment around the module or system
containing the camera. Each of these purposes,
however, requires a different type of hardware.
For visualization, the algorithms needed to
transform the raw data into a visible image by
the human eye have existed for a long time and
are optimized in terms of performance or quality.
The hardware has evolved in parallel, and today
is embodied in Image Signal Processors (ISPs),
allowing processing from the pixels of the image.
For analysis, however, new algorithms require
computing power to achieve the precision sought
in understanding the surrounding environment.
That’s especially true for algorithms derived from
artificial intelligence techniques such as deep
learning. This is where the Vision Processor (VP)
comes in. Its goal is to analyze a complete frame,
not just the pixel level.
In this report, Yole Développement will segment
processing and computing respectively according
to their association with the image signal processor
and vision processor. At the business level,
segmentation is quite simple. Some companies
offer a license and royalties for a design, which
is known as intellectual property (IP) business.
Other companies sell the chips, which we call the
silicon business.
IMAGE SIGNAL PROCESSOR AND VISION PROCESSOR
MARKET AND TECHNOLOGY TRENDS 2019
Market & Technology Report - June 2019
Artificial intelligence-powered newcomers are reshuffling the pack.
REPORT KEY FEATURES
• Intellectual property and silicon
business models description and
market dynamics
• Image signal processor and vision
processor market dynamics
• Technology trends and future
outlook
• Descriptions of ecosystem and
applications
• Image processing pipeline and deep
learning descriptions
(Yole Développement, June 2019)
Processing and computing hardware types
WHAT ARE WE TALKING ABOUT?
(Pre-)Process data
Processing
Out of report scope
Analyze data
Computing
Image signal processor
Data type
Gathering data
Raw
Vector
Object
Computing
power
TOPSGFLOPSMFLOPSØ
Vision processor
Standalone chip
Embedded with
sensor
Embedded in
application processor
Embedded in
application processor
Standalone chip
AI has completely disrupted hardware in vision
systems, and has had an impact on entire
segments, as Mobileye has in automotive, for
example. Image analysis adds a lot of value.
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 ISP market offers a steady
compound annual growth rate (CAGR) from
WHAT ARE THE MARKET DYNAMICS?
27. IMAGE SIGNAL PROCESSOR AND VISION PROCESSOR MARKET AND TECHNOLOGY TRENDS 2019
Image hardware: 2018-2024 volumes and revenues
(Yole Développement, June 2019)
2018 to 2024 of 3%, making the total market worth
$4.2B in 2024. Meanwhile, the vision processor
market is exploding, with a 18% CAGR from 2018
to 2024, making the market worth $14.5B in 2024!
(Yole Développement, June 2019)
ISP
74%
Vision
processor
26%
ISP
55%
Vision
processor
45%
2018
3 Bunits
2024
4.5 Bunits
ISP : Image Signal Processor
CAGR: Compound Annual Gross Rate
Volume
shipments
Revenues ISP
37%
Vision
processor
63%
2018
$9.5 B
ISP
23%
Vision
processor
77%
2024
$18.6 B
CAGR2018-2024
: 7%
CAGR2018-2024
: 12%
Historical players used to
build design ISP* for
viewing but late in
developing powerful vision
processors.
Why?
Usually, these companies
are used to work in long
cycles segment like
automotive, rise of AI was
too fast.
So
what?
2 types
of players IP** companies used the
rise of AI to design
dedicated architecture.
Startups like Mobileye used
this lack of computing too.Processing
Computing
2018: all companies propose a vision processor product.
5 years after Mobileye: historical companies use IP to catch up
and begin to deliver their own architecture.
Finally…
* ISP: Image Signal Processor
** IP: Intellectual Property
Competitive landscape – Key players dynamics
Processing and computing hardware for the imaging
market has been divided into two different business
models. IP companies don’t have physical products,
but silicon companies sell the physical processors. The
leaders are easy to identify for each category. ARM and
Synopsys lead the IP segment and OmniVision, Mobileye
and ON Semiconductor lead the silicon segment.
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 mobile device, automotive,
computing and surveillance industries. The applications
include biometry and photography, autonomous
driving, behavioral recognition, human identification
and tracking.
It is important to note that historical players have
struggled to react to AI’s arrival. That has allowed
other companies to get into the business, including
smartphone companies like Apple and Huawei,
startups like Mobileye, and companies in other
segments, like NVIDIA in automotive applications.
However, because the trend is towards low-power,
low-consumption, always-on computing hardware,
the historical players are coming back into the game.
AI technologies promise a bright future in many areas,
with rapid software and hardware progress. This 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.
A UNUSUAL ECOSYSTEM HAS BEEN CREATED
28. MARKET TECHNOLOGY REPORT
COMPANIES CITED IN THE REPORT (non exhaustive list)
Amazon, Altek, Ambarella, Apple, ARM, Bosch, CEVA, Canon, ChipsMedia, Continental, Delphi, Denso,
GestureTek, Google, Intel, Imagination, Kalray, Mediatek, Intel Mobileye, Intel Movidius, Nec, Nextchip,
Nikon, NVIDIA, NXP, Oculus, Omek, OmniVision, ON Semiconductor, Panasonic, Pixelworks, Qualcomm,
Quanergy, Samsung, Socionext, Sony, STMicroelectronics, Sunplus, Synopsys, Xilinx, Xperi, and others.
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• From Image Processing to Deep Learning,
Introduction to Hardware and Software
• Status of the Camera Module Industry 2019 –
Focus on Wafer Level Optics
• Hardware and Software for AI 2018 –
Consumer focus
• Imaging for Automotive 2019
• Artificial Intelligence Computing for Automotive
2019
Find all our reports on www.i-micronews.com
AUTHOR
TABLE OF CONTENTS (complete content on i-Micronews.com)
Report objectives and methodology 4
Executive summary 12
Introduction 50
Market forecast 67
Intellectual property market description
Intellectual property market revenues by player
Silicon market revenues by player
IP and silicon market shares
Intellectual property market revenues per
technology
Silicon market revenues by technology
Image signal processor and vision processor
market description
Intellectual property market revenues by
application
Silicon market revenues by application
Market and technology trends 80
Mobile
- Image signal processors in mobile
- Forecast 2012 – 2024 ISP volume shipments in
Munits by type of mobile
- Assumptions on average selling price
- Forecast 2012 – 2024 Image signal processors
revenues in $M
- AI in mobile devices
- Vision processors in mobile devices
- Forecast 2012 – 2024 VP volume shipments in
Munits by type of mobile
- Assumptions on average selling price
- Forecast 2012 – 2024 VP revenues in $M
Automotive
- Vision processing and sensing systems
- Image signal processor
- Forecast 2018 – 2024 ISP volume shipments and
number of ISPs per system assumptions
- Forecast 2018 – 2024 ISP revenues in $M and
ASP assumptions
- Vision processor dedicated to level 2 and level 3
- Forecast 2018 – 2024 Assumptions for ASP of
vision processors
- Forecast 2018 – 2024 VP penetration rate
- Forecast 2018 – 2024 VP volume shipments in
Munits by level of autonomy
- Forecast 2018 – 2024 VP revenues in $M by
level of autonomy
- Forecast 2018 – 2024 ISP and VP volume
shipments in Munits
- Forecast 2018 – 2024 ISP and VP revenues in M$
Other applications
- Volumes, penetration rate and average selling
prices assumptions
- Teardowns
- Forecast 2012 – 2024 ISP and VP volume
shipments in Munits
- Forecast 2012 – 2024 ISP and VP revenues in $M
- Forecast 2012 – 2024 ISP average selling prices
and penetration rates assumptions
- Forecast 2012 – 2024 ISP volume shipment in
Munits per application
- Forecast 2012 – 2024 ISP revenues in $M per
application
- Forecast 2012 – 2024 VP average selling prices
and penetration rates assumptions
- Forecast 2012 – 2024 VP volume shipment in
Munits per application
- Forecast 2012 – 2024 VP revenues in $M per
application
Ecosystems 154
Mobile
Automotive
Technologies 179
Image processing
Image analysis
Conclusion 206
Appendix – Yole Développement’s
presentation 209
REPORT OBJECTIVES
Provide a clear understanding of the image processor and vision processor technologies and market.
• Ecosystem identification and analysis:
- Determine market dynamics
- Technical market description
- Economic requirements by segment
- Key players by market: analysis
- Market size and market forecast in $M
• Analysis and description of the market and technologies involved:
- Major players on a global basis
- Technology identification for different devices and processes
- Competing technologies
- Main technical challenges
- Future directions
As a Technology Market Analyst,
Yohann Tschudi, PhD is a member
of the Semiconductor Software division
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).
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