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A Vision System on Chip (VSoC) is a type of integrated circuit that combines the functions of a processor, image sensor, and image processing algorithms into a single device.
This allows for image processing to be done directly on the chip, which can reduce power consumption, cost, and size compared to traditional systems that require multiple components.
VSoCs are designed to process images from cameras and other types of sensors, such as radar, infrared, and ultrasound. They can also be used for vision-based navigation, computer vision, object recognition, and other applications.
VSoCs typically feature a dedicated image processing unit (IPU) with dedicated hardware accelerators that are optimized for image processing tasks, such as image filtering, edge detection, and motion estimation.
VSoCs are becoming increasingly popular in a variety of industries, such as automotive, medical, industrial, and consumer devices.
By integrating the required components into a single chip, VSoCs can reduce the costs and complexity of development and implementation. Additionally, VSoCs can improve performance by taking advantage of the dedicated hardware accelerators.
In summary, VSoCs are integrated circuits that combine the functions of a processor, image sensor, and image processing algorithms into a single device.
They are designed to reduce cost and complexity while improving performance and power consumption. VSoCs are becoming increasingly popular in a variety of industries, and can be used for a variety of applications such as navigation, computer vision, and object recognition.
The Global Vision Systems on Chip (VSoC) market accounted for $XX Billion in 2022 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2023 to 2030.
The Myriad X VPU (Vision Processing Unit) is a new type of processor released by Movidius that is specifically designed to accelerate computer vision applications.
The Myriad X VPU is built on a 16nm FinFET process node, and it is the first processor to incorporate the Neural Compute Engine, which allows the VPU to perform deep learning tasks. It is capable of up to one trillion operations per second (TOPS) of performance and can support up to 4 TOPS for deep learning applications.
It has a power efficiency of up to 3 TOPS/W, making it one of the most efficient VPUs on the market.
The Myriad X VPU can be used for a wide range of applications, such as image recognition, object detection, autonomous navigation, and facial recognition. It is also capable of streaming video, making it ideal for applications such as security and surveillance.
The Ambarella CVflow Vision SoC series are advanced System on Chip (SoC) solutions designed to enable computer vision applications in a wide range of markets, including security, automotive, surveillance, industrial, and consumer applications. The series includes the CV1, CV2, and CV22 SoCs, each of which is tailored for a specific application.
The CV1 SoC is optimized for advanced image processing and object recognition, while the CV2 SoC and CV22 SoC are optimized for deep learning applications. All three SoCs feature a high-performance, dual-core Arm Cortex-A7 processor and an advanced image processing unit (VPU) for image and video processing.
In addition, the CV2 and CV22 SoC feature Ambarella’s proprietary Neural Network Processing Unit (NPU) for deep learning applications. All three SoCs also support a wide range of sensors, including MIPI CSI, USB, and GigE cameras.
