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
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.
The webinar will wrap up with a live demonstration of the system and questions.
- Introduction on edge processing for modern deep learning applications
- Benefits of using FPGAs as an edge processing system for deep learning applications
- Developing a real time human detection application using Xilinx Zynq MPSoC FPGA
- Live demo