Computer vision chip company Ambarella has announced a new processor in its camera system on chip (SoC) family, targeting what it said is a wider variety of smart device applications requiring flexible artificial intelligence sensing and processing at the edge.
Its new CV28M camera SoC is the latest in its CVflow family, combining advanced image processing, high-resolution video encoding, and computer vision processing in a single, low-power design. Two of the features of the new camera SoC that are especially relevant for intelligent edge sensing and low bandwidths are its AI-based rate control to optimize image quality while reducing video storage and network bandwidth requirements – particularly useful in IP security cameras; and its AI Timelapse, which uses scene analysis to record only what it considers are useful moments and avoiding the time needed to scan through video timelines to retrieve moments of interest.
Jerome Gigot, director of marketing for Ambarella, said, “AI-based bitrate control is the ability to use the Ambarella CVFlow AI engine to analyze every frame before encoding to find the most important regions of each frame (such as a face or a car for example) and be able to encode that specific region of the video with higher quality compared to other regions that don’t contain any important information, such as the background or other unimportant objects.”
In addition, Chris Day, vice president of marketing and business development at Ambarella, explained to embedded.com that the new device uses the same AI engine and capability as its CV25, but is addressing new cost-conscious markets and enabling a new class of smart edge devices for applications including smart home security, retail monitoring, consumer robotics, and occupancy monitoring.
“All around us, devices are becoming smarter, and with our newest CV28M SoC, our customers can develop a new generation of intelligent sensing cameras for a variety of new applications. In privacy-sensitive applications—such as monitoring retail stores, workplaces, rental properties, or the elderly at home—edge-based AI processing can support intelligent monitoring and fast decision-making without the requirement to record or stream video to the cloud.”
He said the main difference with the new camera SoC is that it uses a dual core Arm Cortex-A53 as opposed to a quad-core device. This results in greater efficiency for edge sensing AI, and up to 30% reduction in cost. He added that for many of the more consumer-orientated embedded vision applications, they don’t need the full AI capability of the CV25, so the new CV28M provides a more targeted approach.
For AI sensing applications such as retail monitoring or occupancy monitoring, CV28M provides the AI performance to make all decisions in the camera, preserving privacy and avoiding heavy video processing running on back-end servers. In consumer robotics applications, the CV28M can be connected to a wide range of sensors such as visible, structured light, and time-of-flight (ToF) to capture, and then process, the data required for navigation.
The CV28M delivers efficient video encoding in both AVC and HEVC formats. A high-performance image signal processor (ISP) delivers outstanding imaging in low light conditions, and high dynamic range (HDR) processing extracts maximum image detail in high-contrast scenes. The camera SoC includes a full suite of advanced cybersecurity features to protect against hacking including secure boot, TrustZone and I/O virtualization. Fabricated in 10 nm ultralow-power process technology, the CV28M chip is optimized for wire-free camera applications that require long battery life and small form factors.
The CV28M chip shares a common SDK and computer vision (CV) tools with Ambarella’s CV25, CV22, and CV2 CVflow SoC families, simplifying development of cameras with multiple price and performance options. A complete set of CV tools helps customers port their own neural networks onto CV28M and includes a compiler, debugger, and support for industry-standard machine learning frameworks such as Caffe and TensorFlow, with extensive guidelines for convolutional neural network (CNN) performance optimizations.
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