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Last Updated: Apr 25, 2025 | Study Period:
AI vision processors are a specialised subset of AI processors created especially to handle tasks involving visual input. While there are many different AI tasks that can be handled by AI processors, vision AI processors are designed specifically to handle tasks like image processing, video recognition, and object detection.
A new class of microprocessors called AI chips were developed primarily to perform activities requiring artificial intelligence more rapidly and effectively.
Artificial intelligence (AI)'s field of computer vision enables computers and systems to extract useful information from digital photos, videos, and other visual inputs and to conduct actions or offer recommendations in response to that information.
How much data it can handle, what kind of data it can process, and how many simultaneous calculations it can perform are what truly distinguish a regular chip from an AI chip. In order to facilitate effective deep learning computing, new AI chip architectures are being driven by new software AI algorithmic advances.
Even with active investment and a large number of participating manufacturers, China's AI chip industry still has some difficulties developing and keeping up with American and European firms.
| S No | Company Name | Development |
|---|---|---|
| 1 | Hailo | Hailo, a leader in the development of edge artificial intelligence (AI) chips, launched its revolutionary new Hailo-15 series of high-performance vision processors. These processors are intended to be directly integrated into intelligent cameras to provide cutting-edge video processing and analytics. |
| 2 | Inuitive Ltd | Inuitive Ltd, a provider of Vision-on-Chip processors, recently announced the launch of the NU4100, a new addition to its line of Vision and AI ICs. The new NU4100 IC features integrated dual-channel 4K ISP, improved AI processing, and depth sensing in a single-chip, low-power design, setting a new industry benchmark for Edge-AI performance. It is based on Inuitive's distinctive architecture and cutting-edge 12nm manufacturing technology. |
| 3 | Ambarella | Ambarella introduced a new computer vision processor for artificial intelligence processing at the edge of computer networks, such as in smart cars and security cameras. The most recent member of the company's CVflow series is the new CV28M camera system on chip (SoC). It integrates sophisticated image processing, high-resolution video encoding, and computer vision processing in a single, low-power semiconductor. |
| 4 | Sony | Sony has introduced the first intelligent vision sensors with AI processing capabilities. A layered structure of a logic chip and pixel chip is present in the new sensor goods. They are the first image sensor in the world to have functionality for AI image processing and analysis built into the logic chip. In order to create edge AI systems, high-performance CPUs and external memory are not required because the signal obtained by the pixel chip is processed using AI on the sensor. |
With the introduction of Hailo-15, the business is revolutionising the smart camera sector by establishing new benchmarks for computer vision and deep learning video processing, enabling unheard-of AI performance in a variety of applications for various industries.
Hailo-15 enables smart city operators to more quickly detect and respond to incidents, manufacturers to boost output and machine uptime, retailers to safeguard supply chains and boost customer satisfaction, and transportation authorities to identify everything from lost children to accidents to misplaced luggage.
The second iteration of the NU4x00 family of products is the NU4100. Robotics, drones, virtual reality, and edge-AI applications that call for numerous sensor aggregation, processing, packing, and streaming are the perfect fit for the NU4x00 series.
It is especially made for robots and other applications that need to use three, six, or more cameras to sense and analyse their surroundings in order to make quick judgements.
Ambarella crammed a lot of AI processing power into the chip to foresee how computer networks will change as more and more things are connected to the internet. Self-driving cars, for example, will have to perform their processing at the edge of the network, or in the car itself, rather than heavily engaging with datacenter processors, due to the possibility that networks would get overwhelmed with data traffic.
In order to reduce data bulk and allay any privacy worries, the sensor generates metadata (semantic information associated with visual data). In addition, the ability to use AI enables the delivery of a variety of functionalities for a variety of applications, such as real-time object tracking with quick AI processing.
The Global AI-vision chip market accounted for $XX Billion in 2023 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2024 to 2030.
Atom is a brand-new AI chip from Rebellions. The business created the new chip in an effort to take on US chipmaker Nvidia, which currently dominates the AI chip market. In addition to providing optimised software, Rebellions wants to offer a domain-specific AI processor.
The most powerful yet energy-efficient AI hardware as well as seamless software integration will be available to clients thanks to this strategy and specially decentralised programming methodology, the Atom CPU is "designed to excel at running chatbot AI applications."
"The processor uses just around 20% of the power of an Nvidia A100 chip on those operations because it focuses on a narrow range of jobs rather than doing a large range,so Nvidia AI chips will face competition from Rebellions' Atom AI chip.
The semiconductor industry's fastest expanding market is for AI processors. Having competition in this market is a positive thing.
| Sl no | Topic |
| 1 | Market Segmentation |
| 2 | Scope of the report |
| 3 | Abbreviations |
| 4 | Research Methodology |
| 5 | Executive Summary |
| 6 | Introdauction |
| 7 | Insights from Industry stakeholders |
| 8 | Cost breakdown of Product by sub-components and average profit margin |
| 9 | Disruptive innovation in theIndustry |
| 10 | Technology trends in the Industry |
| 11 | Consumer trends in the industry |
| 12 | Recent Production Milestones |
| 13 | Component Manufacturing in US, EU and China |
| 14 | COVID-19 impact on overall market |
| 15 | COVID-19 impact on Production of components |
| 16 | COVID-19 impact on Point of sale |
| 17 | Market Segmentation, Dynamics and Forecast by Geography, 2024-2030 |
| 18 | Market Segmentation, Dynamics and Forecast by Product Type, 2024-2030 |
| 19 | Market Segmentation, Dynamics and Forecast by Application, 2024-2030 |
| 20 | Market Segmentation, Dynamics and Forecast by End use, 2024-2030 |
| 21 | Product installation rate by OEM, 2023 |
| 22 | Incline/Decline in Average B-2-B selling price in past 5 years |
| 23 | Competition from substitute products |
| 24 | Gross margin and average profitability of suppliers |
| 25 | New product development in past 12 months |
| 26 | M&A in past 12 months |
| 27 | Growth strategy of leading players |
| 28 | Market share of vendors, 2023 |
| 29 | Company Profiles |
| 30 | Unmet needs and opportunity for new suppliers |
| 31 | Conclusion |
| 32 | Appendix |
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