Xilinx, Inc. has expanded into broad range of vision guided machine learning applications with the introduction of reVISION stack. This enables machine learning from the edge to the cloud.
The new reVISION stack enables engineers to develop intelligent vision guided systems easier and faster, with little or no hardware design expertise. The engineers can now utilize the advantages of combining machine learning, computer vision, sensor fusion, and connectivity.
reVISION enables applications spanning a number of markets such as high end consumer, automotive, industrial, medical, and aerospace & defense. Next generation applications include collaborative robots or 'cobots', 'sense and avoid' drones, augmented reality, autonomous vehicles, automated surveillance and medical diagnostics. Applications in these markets require systems to be extremely responsive and algorithms and sensors need to be quickly deployed.
Developers with limited hardware expertise can use a C/C++/OpenCL development flow with industry-standard frameworks and libraries like Caffe and OpenCV to develop embedded vision applications on a single Zynq SoC or MPSoC. Also, developers can leverage the advantages of reconfigurability and any-to-any connectivity to develop and deploy upgrades.
The Xilinx reVISION stack includes support for neural networks including AlexNet, GoogLeNet, SqueezeNet, SSD, and FCN. The stack provides library elements including pre-defined and optimized implementations for CNN network layers, required to build custom neural networks (DNN/CNN).
"We are seeing tremendous interest in machine learning from the edge to the cloud, and believe that our ongoing investment in development stacks will accelerate mainstream adoption," said Steve Glaser, SVP of Corporate Strategy at Xilinx. "Today, hundreds of embedded vision customers have realized greater than 10x performance and latency advantages with Xilinx technology. With the addition of reVISION, those same advantages will now become available to thousands of customers."
The reVISION stack will be available in the second quarter of 2017.