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INTRODUCTION TO EDGE AI SoC MARKET
Edge AI SoCs are semiconductor chips that offer advanced Artificial Intelligence (AI) capabilities and are specifically designed to be used at the edge of the network. Edge AI SoCs are used to enable real-time AI applications such as facial recognition, object detection, and speech recognition. They are capable of running AI algorithms locally without the need for cloud computing.
The edge AI SoC is a combination of hardware and software components. The hardware component is a system-on-chip (SoC) that is used to power the AI system. It includes an embedded processor, a graphics processing unit (GPU), and a field programmable gate array (FPGA). The software component consists of an AI-specific operating system, an AI framework, and an AI model.
The edge AI SoC is designed to be extremely energy-efficient and to provide fast machine learning (ML) inference capabilities. It can be used to power a wide range of AI applications such as autonomous vehicles, medical devices, robotics, and smart home applications. It also enables low-latency decision making and fast response times, which are essential for real-time AI applications.
The edge AI SoC is becoming increasingly popular as it can be used to provide AI capabilities in a cost-effective and energy-efficient manner. It is also becoming a go-to choice for AI applications as it offers a lower latency than cloud-based solutions and can be used in environments where internet connectivity is unreliable.
EDGE AI SoC MARKET SIZE AND FORECAST
The Global Edge AI SoC 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.
With new records for AI performance per watt, image quality, and sensor fusion, Ambarella introduced a 4K, 5nm Edge AI SoC for mainstream security cameras. Ambarella’s new CV72S SoC, which combines radar and video data for improved nighttime and all-weather AI perception, boasts the highest AI performance per watt in the security sector thanks to its architecture.
For many vision tasks, the current transformer neural networks (NNs) perform more effectively than convolutional NNs thanks to the hardware specialised to artificial intelligence (AI).
Furthermore, with 6x more AI performance than its predecessor, the CV72S can run Ambarella’s ground-breaking AISP for neural network-enhanced 4K, long-range colour night vision and HDR at very low lux levels with little noise and no external illumination, all the while providing ample headroom for other usage.
The highest AI performance per watt for the mainstream security market is provided by the CV72S, which uses less than 3W of power due to the efficiency of the CVflow architecture and 5nm production technology.
Ambarella’s CV72S is a great example of how security cameras are continuing to incorporate an increasing amount of AI capabilities at the edge through camera processors, all while keeping a low power and thermal budget.
Designers are searching for a solution that can cover larger areas in all weather and lighting circumstances, improve image quality, and increase AI performance without adding to their power budgets in order to elevate their professional security cameras from the mainstream to the next level.
By incorporating its most recent CVflow 3.0 architecture and 5nm process technology into the new CV72S SoC—which was designed with the mainstream security industry in mind—and leveraging our expertise in visual processing, radar, and artificial intelligence, they are speeding up their innovation.
Ambarella created the CV72S with the powerful 16MP30 fisheye dewarping and 4x 5MP30 multi-imager AI capabilities of higher-end cameras in mind, due to its extensive knowledge and expertise in the security sector.
With a 2x improvement in performance over its predecessor, the CV72S offers 4KP60 encoding for AVC and HEVC for single-imager cameras. These characteristics make them perfect for a variety of commonplace security camera applications, such as traffic and smart city monitoring and crowd surveillance.
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