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Title: How to develop a real time human detection application on an FPGA edge device using deep learning

Description: The ability to process at the edge is critical to many modern applications such as smart traffic cameras, assisted/autonomous driving and vision guided robotics. While these use cases may implement different high-level algorithms, all of them require a common foundation as follows: • Interfacing with one or more high resolution, high bandwidth image sensors to capture images • Processing the captured image to convert from RAW pixel data to RGB and onwards into a format which is suitable for further processing of the specific algorithm. • Implement Convolution Neural Network Machine Learning Inference algorithms classifying objects of interest for the use case • Recording data to nonvolatile memory to provide event log/buffer of the application history for diagnosis or later analysis. This webinar will demonstrate an example implementation by using the Aldec TySOM-3A-ZU19EG, FMC-ADAS and FMC-NVMe daughter cards where these applications can be easily prototyped on a Xilinx Zynq UltraScale+ MPSoC. In this webinar you’ll learn: • Benefits of using FPGAs for inference deep learning object detection on the edge • How to capture video into the FPGA using automotive HSD camera, 192-degree wide lens • How to implement a deep learning human detection algorithm inside FPGA and accelerate it with the power of Xilinx Deep Learning Processor Unit (DPU) • How to record/buffer the processed data at high speed over the PCIe to a NVMe SSD card • How to display the output of the system on a HDMI monitor This webinar will explore each stage of the implementation explaining in detail the design implementation and engineering decisions required to create such a solution. Of course, along with demonstrating the benefits of such an approach for edge processing systems.

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