Today, Xilinx announced a comprehensive suite of industry-standard resources for developing advanced embedded-vision systems based on machine learning and machine inference. It’s called the reVISION stack and it allows design teams without deep hardware expertise to use a software-defined development flow to combine efficient machine-learning and computer-vision algorithms with Xilinx All Programmable devices to create highly responsive systems. (Details here.)
The Xilinx reVISION stack includes a broad range of development resources for platform, algorithm, and application development including support for the most popular neural networks: AlexNet, GoogLeNet, SqueezeNet, SSD, and FCN. Additionally, the stack provides library elements such as pre-defined and optimized implementations for CNN network layers, which are required to build custom neural networks (DNNs and CNNs). 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, which support various sensor types.
The reVISION development flow starts with a familiar, Eclipse-based development environment; the C, C++, and/or OpenCL programming languages; and associated compilers all incorporated into the Xilinx SDSoC development environment. You can now target reVISION hardware platforms within the SDSoC environment, drawing from a pool of acceleration-ready, computer-vision libraries to quickly build your application. Soon, you’ll also be able to use the Khronos Group’s OpenVX framework as well.
For machine learning, you can use popular frameworks including Caffe to train neural networks. Within one Xilinx Zynq SoC or Zynq UltraScale+ MPSoC, you can use Caffe-generated .prototxt files to configure a software scheduler running on one of the device’s ARM processors to drive CNN inference accelerators—pre-optimized for and instantiated in programmable logic. For computer vision and other algorithms, you can profile your code, identify bottlenecks, and then designate specific functions that need to be hardware-accelerated. The Xilinx system-optimizing compiler then creates an accelerated implementation of your code, automatically including the required processor/accelerator interfaces (data movers) and software drivers.
The Xilinx reVISION stack is the latest in an evolutionary line of development tools for creating embedded-vision systems. Xilinx All Programmable devices have long been used to develop such vision-based systems because these devices can interface to any image sensor and connect to any network—which Xilinx calls any-to-any connectivity—and they provide the large amounts of high-performance processing horsepower that vision systems require.
Initially, embedded-vision developers used the existing Xilinx Verilog and VHDL tools to develop these systems. Xilinx introduced the SDSoC development environment for HLL-based design two years ago and, since then, SDSoC has dramatically and successfully shorted development cycles for thousands of design teams. Xilinx’s new reVISION stack now enables an even broader set of software and systems engineers to develop intelligent, highly responsive embedded-vision systems faster and more easily using Xilinx All Programmable devices.
And what about the performance of the resulting embedded-vision systems? How do their performance metrics compare against against systems based on embedded GPUs or the typical SoCs used in these applications? Xilinx-based systems significantly outperform the best of this group, which employ Nvidia devices. Benchmarks of the reVISION flow using Zynq SoC targets against Nvidia Tegra X1 have shown as much as:
6x better images/sec/watt in machine learning
42x higher frames/sec/watt for computer-vision processing
1/5th the latency, which is critical for real-time applications
There is huge value to having a very rapid and deterministic system-response time and, for many systems, the faster response time of a design that's been accelerated using programmable logic can mean the difference between success and catastrophic failure. For example, the figure below shows the difference in response time between a car’s vision-guided braking system created with the Xilinx reVISION stack running on a Zynq UltraScale+ MPSoC relative to a similar system based on an Nvidia Tegra device. At 65mph, the Xilinx embedded-vision system’s response time stops the vehicle 5 to 33 feet faster depending on how the Nvidia-based system is implemented. Five to 33 feet could easily mean the difference between a safe stop and a collision.
The last two years have generated more machine-learning technology than all of the advancements over the previous 45 years and that pace isn't slowing down. Many new types of neural networks for vision-guided systems have emerged along with new techniques that make deployment of these neural networks much more efficient. No matter what you develop today or implement tomorrow, the hardware and I/O reconfigurability and software programmability of Xilinx All Programmable devices can “future-proof” your designs whether it’s to permit the implementation of new algorithms in existing hardware; to interface to new, improved sensing technology; or to add an all-new sensor type (like LIDAR or Time-of-Flight sensors, for example) to improve a vision-based system’s safety and reliability through advanced sensor fusion.