Automotive camera SoC handles Deep Neural Networks at low power consumption

December 17, 2018 //By Christoph Hammerschmidt
Automotive camera SoC handles Deep Neural Networks at low power consumption
Ambarella, Inc. (Santa Clara, Calif.) a fabless chip company focusing on video processing and computer vision, has introduced the CV22AQ automotive camera System-on-Chip (SoC). The device is featuring the company’s CVflow computer vision architecture for 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 CV22AQ 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, the company claims. Fabricated in advanced 10nm process technology, its low power consumption supports the small form factor and thermal requirements of windshield-mounted forward ADAS cameras.

According to Ambarella CEO Fermi Wang, to date, front ADAS cameras have been performance-constrained due to power consumption limits inherent in the form factor. Despite these restrictions, the new CV22AQ provides a combination of outstanding neural network performance and very low typical power consumption of below 2.5 watts, he added. This 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.

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