Xilinx and Motovis offer complete automotive forward camera solution - Embedded.com

Xilinx and Motovis offer complete automotive forward camera solution

Xilinx and Motovis collaborate to pair Zynq SoC with Motovis’ CNN IP for automotive vision, to provide a complete hardware and software solution for forward camera systems in vehicles.

As part of Xilinx’ ongoing initiative to provide more platforms and solutions that customers can use out of the box and customize for their requirements, Xilinx has announced it is collaborating with Motovis, a developer of artificial intelligence (AI) algorithms for automotive computer vision, to offer a complete hardware and software solution for forward camera systems in vehicles.

The two companies have paired the Xilinx Automotive (XA) Zynq system-on-chip (SoC) platform and Motovis’ convolutional neural network (CNN) intellectual property (IP) for the automotive market, to provide a solution specifically for forward camera systems’ vehicle perception and control. The forward camera solution scales across the 28nm and 16nm XA Zynq SoC families using Motovis’ CNN IP, combining optimized hardware and software partitioning capabilities with customizable CNN-specific engines that host Motovis’ deep learning networks – resulting in a cost-effective offering at different performance levels and price points.

The solution supports image resolutions up to eight megapixels. For the first time, OEMs and Tier-1 suppliers can now layer their own feature algorithms on top of Motovis’ perception stack to differentiate and future-proof their designs.

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Forward camera systems are a critical element of advanced driver-assistance systems because they provide the advanced sensing capabilities required for safety-critical functions, including lane-keeping assistance (LKA), automatic emergency braking (AEB), and adaptive cruise control (ACC). The solution, which is available now, supports a range of parameters necessary for the European New Car Assessment Program (NCAP) 2022 requirements by utilizing convolutional neural networks to achieve a cost-effective combination of low-latency image processing, flexibility and scalability.

Market forces continue to drive adoption of forward camera systems to adhere to global government mandates and consumer watch groups including The European Commission General Safety Regulation, the National Highway Traffic Safety Administration and the NCAP. All three have issued formal mandates or strong guidance regarding automakers’ implementations of LKA and AEB in new vehicles produced between 2020-2025 and onward.

Commenting on why the pairing of hardware and software from the two companies is significant, analyst Ian Riches said, “This collaboration is a significant milestone for the forward camera market as it will allow automotive OEMs to innovate faster.” Riches, who is vice president for the global automotive practice at Strategy Analytics, added, “The forward camera market has tremendous growth opportunity, where we anticipate almost 20 percent year-on-year volume growth over 2020 to 2025. Together, Xilinx and Motovis are delivering a highly optimized hardware and software solution that will greatly serve the needs of automotive OEMs, especially as new standards emerge and requirements continue to grow.”

Willard Tu, senior director of automotive for Xilinx, said, “Expanding our XA [referring to Xilinx Automotive] offering with a comprehensive solution for the forward camera market puts a cost-optimized, high-performance solution in the hands of our customers. Motovis’ expertise in embedded deep learning and how they’ve optimized neural networks to handle the immense challenges of forward camera perception puts us both in a unique position to gain market share, all while accelerating our OEM customers’ time to market.”

The CEO of Motovis, Zhenghua Yu, added, “We are extremely pleased to unveil this new initiative with Xilinx and to bring to market our CNN forward camera solution. Customers designing systems enabled with AEB and LKA functionality need efficient neural network processing within an SoC that gives them flexibility to implement future features easily. With Motovis’ customizable deep learning networks and the Xilinx Zynq platform’s ability to host CNN-specific engines that provide unmatched efficiency and optimization, we’re helping to future-proof the design to meet customer needs.”

Xilinx and Motovis will be speaking at the Xilinx Adapt 2021 virtual event on September 15, 2021, which is expected to feature over 100 presentations, forums, product trainings and labs from  Xilinx, its partners and customers.


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