Xilinx is hosting a webinar about the Vivado HLS Video Libraries. The webcast will show how to rapidly accelerate real-time computer vision algorithms and integrate them into designs using Xilinx Zynq-7000 All Programmable SoC devices. The title of the webinar is: How to Accelerate OpenCV Applications with Zynq-7000 All Programmable SoC using Vivado HLS Video Libraries. The online seminar will take place Wednesday, August 28, 2013 at 8am PST (11am EST, 3 GMT).
Vivado HLS Video Libraries Webcast Topics
- Xilinx Smarter Vision solutions’ for computer vision
- How to implement OpenCV functions using Zynq-7000 All Programmable SoCs
- How to accelerate OpenCV function calls by replacing them with HLS-synthesizable function calls that run as much as 700x faster than the same functions implemented in software
- How to refactor OpenCV functions by encapsulating them in a hardware accelerator with standard AXI4 interfaces and easily connect these hardware locks to the rest of your system
- How to rapidly integrate Vivado HLS created accelerator functions using the Vivado IP Integrator in a reference design based on the Zynq-7000 All Programmable SoC ZC702 Eval Board
The webinar includes step-by-step examples that will show engineers how to build and integrate custom video accelerators by migrating functions from the OpenCV library or their own custom video algorithms into embedded design using guaranteed-to-synthesize HLS (High-level Synthesis) video libraries; the Vivado HLS compiler; and the Vivado IP Integrator. The design flow enables designers to implement real-time embedded computer-vision algorithms using a seamless combination of high-performance, low-power, on-chip programmable hardware for high-data-rate (full HD) pixel-processing tasks and software-programmable ARM processor cores for frame-based processing tasks that operate at lower data rates.
The online seminar speakers are Stephen Neuendorffer, Principal Engineer, Vivado High-Level Synthesis, Xilinx and Girish Malipeddi, Video Solutions Marketing Manager, Xilinx.