The Xilinx reVISION stack includes a range of development resources for platform, algorithm and application development. This includes support for the most popular neural networks including AlexNet, GoogLeNet, VGG, SSD, and FCN. Additionally, the stack provides library elements including pre-defined and optimized implementations for CNN network layers, required to build custom neural networks (DNN/CNN). The machine learning elements are complemented by a broad set of acceleration ready OpenCV functions for computer vision processing. For application level development, Xilinx supports industry standard frameworks and libraries including Caffe for machine learning and OpenCV for computer vision. The reVISION stack also includes development platforms from Xilinx and third parties, including various types of sensors. For more information go to the Xilinx reVISION webpage.
This Getting Started Guide complements the 2018.2 version of the ZCU102 and ZCU104 single-sensor reVISION platforms. For other versions, refer to the reVISION Getting Started Guide overview page.
2018.2
- Update to 2018.2 SDSoC version
- Update to 2018.2 xfOpenCV libraries version
- Update to 2018.2 Vivado version
- Update to 2018.2 PetaLinux version
- Minor fixes and improvements
To obtain technical support for this reference design, go to the:
- Xilinx Answers Database to locate answers to known issues
- Xilinx Community Forums to ask questions or discuss technical details and issues. Please make sure to browse the existing topics first before filing a new topic. If you do file a new topic, make sure it is filed in the sub-forum that best describes your issue or question e.g. Embedded Linux for any Linux related questions. Please include "ZCU102 reVISION" or "ZCU104 reVISION" and the release version in the topic name along with a brief summary of the issue.
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