Introducing Xilinx’s all new library for computer vision, xfOpenCV , accelerating the most critical OpenCV functions. xfOpenCV will allow you to easily compose and accelerate computer vision functions in the FPGA fabric through SDx or HLx environments. In addition, xfOpenCV library is consistent with OpenCV and are optimized for performance, resource utilization and ease of use. xfOpenCV is available to the public on github.

OpenCV library functions are essential to developing many computer vision applications. Xilinx’s xfOpenCV for computer vision, based on key OpenCV functions, will allow you to easily compose and accelerate computer vision functions in the FPGA fabric through SDx or HLx environments. In addition, xfOpenCV library functions are consistent with OpenCV and are optimized for performance, resource utilization and ease of use.

• Thousands of functions in the OpenCV 3.1 library are available to run on the ARM Cortex™-A9 and Cortex A53 cores in Zynq
• ~45 OpenCV functions (the OpenVX subset) are available as a library of RTL optimized functions for Xilinx SoCs
• Complete library user guide with device utilization and performance
• Support for 1 and 8 pixel parallel versions is available for most functions

Get started today designing your computer vision system around Zynq SoCs/MPSoCs and FPGAs by leveraging existing Xilinx and ecosystem design hardware, modules and production-ready Systems on Module (SOMs).

Join the discussion on Xilinx forums.

Machine learning and deep learning have gained attention from the development community as a technique that provides enhanced intelligence to many applications including Embedded Vision. While not a new discipline, relatively new breakthroughs in algorithms, access to large data sets for algorithm training and efficient and economically more viable computing platforms have resulted in very rapid interest and adoption of the technology.

Xilinx’s Zynq SoCs/MPSoCs are an ideal fit for machine learning, achieving 6X better images/sec/Watt in machine learning inference relative to embedded GPUs and typical SoCs. Xilinx’s reVISION Stack removes traditional design barriers by allowing you to quickly take a trained network and deploy it on Zynq SoCs and MPSoCs for inference. With the tight integration with Deephi ML IP/libraries in SDSoC, reVISION takes a leap forward towards higher productivity.

Features:

• Full software stack for deploying machine learning applications such as classification, detection, tracking and segmentation etc.
• Hardware optimized, and supported commonly used layers include: Conv, Dilated conv, Deconv, Depthwise Conv, ReLU/Leaky Relu/Relu6, Max/Ave Pooling, FC, Element wise, Concat, Batch normalization, Upsampling, Split, Reorg, Resize, SoftMax, Sigmoid etc.
• Easy-to-use DNNDK tool allows the quantization, compilation and deployment of caffe-trained network models to Xilinx Zynq-7000 and ZU+ MPSoC platforms including ZCU102, ZCU104, Ultra96, DPB1303 board
• Optimized reference models available for a wide range of network topologies, such as GoogleNet, Resnet, VGG, Yolo, SSD, Densebox, SqueezeNet, Mobilenet, FPN, Enet, ESPNet as well as some of the pruned models
• Both SDSoC 2018.3 and Vivado can be used to integrate the Machine Learning IP and runtime libraries to your custom platform easily and quickly

Please download DNNDK and board images from http://www.deephi.com/technology/dnndk.

The AI revolution has accelerated the development and evolution of sensor technologies across numerous categories. It has also resulted in a mandate for a new level of sensor fusion, combining multiple types of sensors in different combinations to create a full and complete view of the system’s environment and objects in that environment. Whatever sensor configuration is specified today, or implemented tomorrow, needs to be ‘future proofed’ through hardware reconfigurability. Only Xilinx programmable devices offer this level of reconfigurability.

Zynq-based vision platforms offer robust any-to-any connectivity and sensor interfaces. Zynq sensor and connectivity advantages include:

• Up to 12x more bandwidth relative to alternative SoCs currently in the market, including support for native 8K and custom resolutions
• Significantly more high and low bandwidth sensor interfaces and channels, enabling highly differentiated combinations of sensors such as RADAR, LiDAR, accelerometers and force torque sensors
• Industry leading support for the latest data transfer and storage interfaces, easily reconfigured for future standards

Get started today designing your computer vision system around Zynq SoCs/MPSoCs and FPGAs by leveraging existing Xilinx and ecosystem design hardware, modules and production-ready Systems on Module (SOMs).

Join the discussion on Xilinx forums.

The reVISION Stack includes five computer vision design examples and five machine learning demos, with more to come. These examples are intended to get you up-and-running in a very short period of time. These design examples will help you easily see the distinct advantage Xilinx SoCs have in high performance Embedded Vision applications.To access these examples, follow the reVISION Getting Started

Get started today designing your computer vision system around Zynq SoCs/MPSoCs and FPGAs by leveraging existing Xilinx and ecosystem design hardware, modules and production-ready Systems on Module (SOMs).

Join the discussion on Xilinx forums.

Get started today designing your computer vision system around Zynq SoCs/MPSoCs and FPGAs by leveraging existing Xilinx and ecosystem design hardware, modules and production-ready Systems on Module (SOMs).

Join the discussion on Xilinx forums.