Embedded World: Machine learning engine opens up AI applications

The new reVISION stack enables a much broader set of software and systems engineers, with little or no hardware design expertise to develop intelligent vision guided systems easier and faster.

These engineers can now realise significant advantages when combining machine learning, computer vision, sensor fusion, and connectivity.

reVISION is enabling a fast growing set of applications in markets where differentiation is critical, systems must be extremely responsive, and the latest algorithms and sensors need to be quickly deployed.

This includes approximately two-thirds of the applications for vision focused semiconductors. Applications span 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.

reVISION enables the fastest path to the most responsive vision systems, with up to 6x better images/second/watt in machine learning inference, 40x better frames/second/watt of computer vision processing, and 1/5th the latency over competing embedded GPUs and typical SoCs.

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.

Leveraging the unique advantages of reconfigurability and any-to-any connectivity, developers can use the stack to rapidly develop and deploy upgrades.

Reconfigurability is critical to ‘future proof’ intelligent vision-based systems as neural networks, algorithms, sensor technologies and interface standards continue to evolve at an accelerated pace.

The Xilinx reVISION stack includes a broad range of development resources for platform, algorithm and application development. This includes support for the most popular neural networks including AlexNet, GoogLeNet, SqueezeNet, SSD, and FCN.

Additionally, the stack provides library elements including pre-defined and optimised 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 including Caffe for machine learning and OpenVX for computer vision.

The reVISION stack also includes development platforms from Xilinx and third parties, including various types of sensors.