Ambarella, Inc., a leading developer of high-resolution video processing and computer vision semiconductors, today introduced the CV22AQ automotive camera System-on-Chip(SoC), featuring the Ambarella CVflow computer vision architecture for powerful Deep Neural Network(DNN) processing.
Target applications include front ADAS cameras, electronic mirrors with Blind Spot Detection(BSD), interior driver and cabin monitoring cameras, and Around View Monitors(AVM) with parking assist.
The new SoC provides the performance necessary to exceed New Car Assessment Program (NCAP) requirements for applications such as lane keeping, Automatic Emergency Braking(AEB), intelligent headlight control, and speed assistance functions.
Fabricated in advanced 10nm process technology, its low power consumption supports the small form factor and thermal requirements of windshield-mounted forward ADAS cameras.
|Target applications for the new Ambarella CV22AQ SoC include front ADAS cameras, electronic mirrors with Blind Spot Detection(BSD), interior driver and cabin monitoring cameras, and Around View Monitors(AVM) with parking assist.|
“To date, front ADAS cameras have been performance-constrained due to power consumption limits inherent in the form factor,” said Fermi Wang, CEO of Ambarella.
“CV22AQ provides an industry-leading combination of outstanding neural network performance and very low typical power consumption of below 2.5 watts. This breakthrough in power and performance, coupled with best-in-class image processing, allows tier-1 and OEM customers to greatly increase the performance and accuracy of ADAS algorithms.”
The CV22AQ’s CVflow architecture provides computer vision processing in 8-Megapixel resolution at 30 frames per second, to enable object recognition over long distances and with high accuracy.
CV22AQ supports multiple image sensor inputs for multi-FOV(Field of View) cameras and can also create multiple digital FOVs using a single high-resolution image sensor to reduce system cost. It enables DNNs for object detection, classification(i.e. of pedestrians, vehicles, traffic signs, and traffic lights), tracking, as well as high-resolution semantic segmentation for applications such as free space detection.
The CV22AQ’s high-performance Image Signal Processor(ISP) provides outstanding imaging in low-light conditions while High Dynamic Range(HDR) processing extracts maximum image detail in high-contrast scenes, further enhancing the computer vision capabilities of the chip.
It includes efficient 8-Megapixel encoding in both AVC and HEVC video formats, allowing customers to add video recording and streaming capabilities to their automotive cameras. The SoC’s advanced cyber security features, which include secure boot, TrustZone and I/O virtualization, enable over-the-air updates (OTA) and also protect against hacking.
A complete set of tools is provided to help customers easily port their own neural networks onto the CV22AQ SoC. The toolkit includes a compiler, debugger, and support for industry-standard machine learning frameworks such as Caffe and TensorFlow, with extensive guidelines for DNN performance optimizations.
CV22AQ is currently sampling to leading tier-1 customers and tier-2 algorithm providers. Chip samples with ASIL-B support are targeted to be available in 2019.
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