Xilinx speeds the development of embedded vision
applications in markets where systems must be highly
differentiated, extremely responsive, and able to immediately
adapt to the latest algorithms and image sensors.
– Only with Xilinx
Embedded Vision is one of the most exciting fields in technology today. Xilinx sees embedded vision as a key and pervasive megatrend that is shaping the future of the electronics industry.
Providing machines the ability to see, sense, and immediately respond to the world creates unique opportunities for system differentiation; however, this also creates challenges in how designers create next-generation architectures and bring them to market. Integrating disparate sub-systems including video and vision I/O with multiple image processing pipelines, and enabling these embedded-vision systems to perform vision-based analytics in real time is a complex task that requires tight coordination between hardware and software teams. To remain timely and relevant in the market, leading development teams are exploiting Xilinx’s devices in their next-generation systems to take advantage of the devices’ programmable hardware, software, and I/O capabilities.
Xilinx provides embedded vision developers with a suite of technologies that support both hardware and software design. Xilinx devices include FPGAs, SoCs and MPSoCs.
The Xilinx Vivado HLx design environment supports both hardware and platform developers developing the latest embedded-vision hardware. These tools include support for the industry’s latest high-bandwidth sensor interfaces. Xilinx SDx tools including SDSoC allows software and algorithm developers to develop in familiar Eclipse-based environments in familiar languages like C, C++ and OpenCL.
The Xilinx reVISION Stack builds upon the SDx concept to include support for OpenCV and machine learning inference, including support for the most popular neural networks such as AlexNet, GoogLeNet, SqueezeNet, SSD, and FCN as well as the functional elements required to build custom neural networks (CNNs/DNNs) while permitting design teams to leverage pre-defined and optimized CNN implementations for network layers. This is complemented by a broad set of acceleration-enabled OpenCV functions for computer vision processing.
Familiar embedded C/C++ application environment to rapidly develop Zynq SoCs
Industry's first multi-processing SoC with the highest levels of security and safety
ARM® based processor with the hardware programmability of an FPGA ideal for high-bandwidth video/vision applications
Video system reference design for multi-channel HD and 4K
Get to market even faster with world class intellectual property for video and vision from Xilinx and its Alliance Program
Video system development out-of-the-box with Xilinx and Alliance Program boards and kits
CAGR growth in commercial
drones from 2015 to 2020
Source: Business Insider
units of virtual reality
devices will grow to 38
million units in 2020
of the whole Internet will
be online video in 5 years