Machine learning applications are rapidly expanding into more and more end markets. For cloud applications, Xilinx has introduced a reconfigurable acceleration stack. Today, Xilinx has launched a new reVISION stack for end-to-end applications, which not only allows Xilinx to complete cloud-to-end deployment in the field of vision-oriented machine learning, but more importantly, It enables embedded software and systems engineers with little or no hardware design expertise to develop visually-oriented smart systems more easily and quickly. Once the advantages of machine learning, computer vision, sensor fusion and connectivity are combined, these engineers will benefit from it.
Looking at the world, Xilinx has become the number one choice for many companies to develop advanced embedded vision systems. So far, 23 automakers around the world have deployed advanced embedded vision systems in 85 different models of ADAS systems, and hundreds of other embedded vision customers have deployed Xilinx-based technology in thousands of applications. At least 40 of the advanced embedded vision systems are already developing or deploying machine learning technologies to dramatically increase system intelligence.
Numerous traditional embedded vision applications are undergoing dramatic changes through the use of machine vision and sensor fusion technologies. The reVISION stack enables manufacturers to implement a range of fast-growing applications in a variety of markets, including traditional high-end consumer markets, automotive, and industrial. , medical and aerospace and defense, and next-generation applications such as collaborative robots, drones with "sensing and avoidance" capabilities, augmented reality, autonomous vehicles, automated monitoring and medical diagnostics. In these application markets, differentiation is critical, systems must respond quickly, and the latest algorithms and sensors must be able to be deployed quickly.
These systems typically have three major features: 1) intelligent and efficient immediate responsiveness; 2) flexibility to upgrade to the latest algorithms and sensors; and 3) constant connectivity to other machines and clouds.
In other words, first, the system must not only think, but also respond immediately to the situation. This requires a more consistent view from sensing to processing, analysis, decision making, communication, and control, while efficiently implementing and deploying the latest machine learning techniques to meet the accuracy requirements of 8-bit and deeper.
Second, given the rapid changes in neural networks and related algorithms and the rapid development of sensors, flexibility must be achieved to upgrade systems through the reconfigurability of hardware and software.
Third, since many new systems are connected together (the Internet of Things), they need to communicate with traditional existing devices, communicate with new devices that will be introduced in the future, and be able to communicate in the cloud.
Clearly, only Xilinx's Zynq reprogrammable SoCs and MPSoCs are best suited to meet these system requirements. The reVISION stack makes Xilinx even more powerful, and it supports the fastest development of the most responsive vision system, with machine-learned inferred unit-power image capture speeds compared to the most competitive computing embedded GPUs and typical SoCs. It has been improved by 6 times, the unit power consumption frame rate of computer vision processing has been increased by 40 times, and the delay is reduced to 1/5. Even developers without hardware expertise can develop embedded vision applications on a single Zynq SoC or MPSoC by combining C/C++/OpenCL development processes, industry-standard frameworks, and libraries such as Caffe and OpenCV.
With the reconfigurability and any connectivity advantages unique to the reVISION stack, developers can quickly develop and deploy upgrades. As neural networks, algorithms, and sensor technology and interface standards continue to accelerate, reconfigurability is critical to the "future-proof" intelligent visualization system.
Steve Glaser, senior vice president of strategy and marketing at Xilinx, said: "There are already many customers who have gained a lot of advantages by using Xilinx products, and this advantage is very significant in the market compared to similar products. But customers also need to invest extra or work, and they need to have the appropriate hardware knowledge to be able to really take advantage of this. So to achieve a very broad adoption, we must support the new The programming model, which is software-defined programming, also meets the industry standard library and new frameworks to support machine learning applications. Therefore, it is possible to extend vision through software-defined programming and industry-standard libraries and frameworks. Guided machine learning applications."
Figure 2: Steve Glaser, Senior Vice President, Strategy and Marketing, Xilinx
The Xilinx reVISION stack includes the rich development resources required for platform, algorithm and application development to support the most popular neural networks, including AlexNet, GoogLeNet, SqueezeNet, SSD and FCN. In addition, the stack provides library elements, including a predefined, optimized implementation of the CNN network layer, which is required to build a custom neural network (DNN/CNN). Machine learning elements are complemented by a rich set of OpenCV features that meet the acceleration requirements for computer vision processing. For application layer development, Xilinx supports industry-standard frameworks, including Caffe for machine learning and OpenVX for computer vision. The reVISION stack also includes development platforms provided by Xilinx and third parties, such as various types of sensors.
Robert Chappell, CEO and founder of Eyetech Digital Systems, said: "Our eye tracking technology uses Zynq SoC to support high-definition visual analysis, benefiting a variety of paralyzed patients such as ALS. The new reVISION stack uses algorithmic power to develop algorithms. Providing a variety of new opportunities. This will certainly enable us to further expand our human-computer interaction hardware products and enhance the value of our core eye tracking products."
Lakshmi Mandyam, senior director of ARM segmentation marketing, said: "The embedded market is an evolving application area where algorithms, neural networks and sensor changes require reconfigurability of the target platform. ARMIL-based from Xilinx Zynq technology will enable these applications to be deployed efficiently while accelerating the widespread adoption of innovative machine learning applications from end to cloud."
Yao Jian, founder and CEO of Deep Technology (DeePhi), said: “Shenjian Technology is committed to providing advanced embedded vision solutions for industrial applications such as robots/UAVs and security surveillance. We have developed a complete workflow. Used to deploy deep learning algorithms on FPGAs to achieve collaborative optimization of algorithms, software, and hardware. In the future, we hope to truly give all things intelligence, and Xilinx's All Programmable technology will support us constantly. Adapting and reconfiguring the system to achieve this goal. The complete toolkit included in the reVISION stack makes it easier for our customers to take advantage of fully programmable FPGAs and SoCs – even those without any FPGA development background Effectively deploy trained models. This is a great benefit for building intelligent solutions with FPGAs."
"Our Dobby Pocket UAV-AI Edition integrates sophisticated computer vision and machine learning technologies to provide consumers with a unique experience through gesture control and object and subject tracking," said Yang Jianjun, CEO of Zero Intelligence. Until recently, it was only a more expensive system, and we implemented these complex algorithms into Dobby AI with Zynq All Programmable devices. We are very happy to see Xilinx launch the reVISION stack platform, which will make it easier for our team to support our team. Enhance these key computer vision and machine learning algorithms to help us give Dobby AI a more unique personality. Having a deep-rooted technology partner like Xilinx will ensure that we can continue to develop breakthrough solutions in this area."
Yao Yuan, general manager of Weishi Rui Technology, said: "In recent years, we have seen more and more users developing embedded vision systems in a variety of different applications, including intelligent monitoring, machine vision and advanced driver assistance systems. Sharp's embedded vision solutions based on Xilinx FPGAs and SoCs provide these users with All Programmable's fully programmable, flexible, high-performance and easy-to-use development process to help them quickly launch embedded vision designs. We implemented a CNN-based deep learning algorithm on the Visionary EagleGo platform, further confirming the outstanding performance of the Zynq-7000 SoC and the efficiency of the SDSoC design tool. We have introduced the Zynq UltraScale+ MPSoC System Level Module (ZURA), and The latest reVISION development stack will be used in the subsequent ZU+ MPSoC Vision Solution Suite, a member of the Xilinx Alliance Program Certified Design Services and an authorized training partner that provides embedding based on Xilinx FPGAs, SoCs and MPSoCs. Visual and video solutions and services. Our solutions include Zynqâ€70 00 SoC/Zynq UltraScale+ MPSoC development board and system-level module (SOM) with built-in OpenCV library, and a Zynqâ€7000 SoC-based turnkey smart camera solution, we hope to design services based on our Xilinx products. And the knowledge of local users, allowing more embedded vision developers to benefit from advanced all-programmable solutions."
Steve Glaser, senior vice president of strategy and marketing at Xilinx, said: "We see great interest in machine learning applications from the end to the cloud, and we also believe that Xilinx's ongoing investment in stack development can accelerate mainstream applications. Today, hundreds of embedded vision customers are using Xilinx technology to achieve performance and latency improvements of more than 10 times. With the addition of reVISION, thousands of customers will benefit from these advantages."
The reVISION stack will be available in the second quarter of 2017.
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