Machine learning technology is an important scientific development of artificial intelligence. By improving the performance of specific algorithms in empirical learning, and the more data used for training, the better the results learned, in order to process and analyze large numbers of images or speech. The identified machine learning algorithm data requires a high-speed parallel computing-like neural network supercomputer built by GPU chips, and uses tools such as Tensorflow, Caffe and other deep learning frameworks to develop effective algorithms.
Once the algorithm is obtained, for the design of the embedded system, the algorithm is quickly applied to the cloud data center, or directly to the edge computing (Edge CompuTIng) device of the terminal, which is the display field of the artificial intelligence application. Due to the continuous improvement of the computing power and low power consumption of the embedded system itself, the intelligent functions and software and hardware solutions built are very helpful to create an artificial intelligence application like never before.
Machine learning techniques have been widely used in data exploration, computer vision, natural language processing, biometrics, search engines, medical diagnostics, detection of credit card fraud, securities market analysis, speech and handwriting recognition, strategy games and robotics.
Programmable Gate Array (FPGA) solutions, leveraging their programmability, are widely recognized in the industry as Xilinx is a major supplier of FPGA solutions. Industry trends led by machine learning, as well as important application examples. Mr. Andy Luo, senior manager of the industrial and medical market in Xilinx Asia Pacific, was interviewed by Xilinx products in the field of machine learning, and outlined the two main application areas.
Andy Luo, Senior Manager, Industrial and Medical Markets, Xilinx Asia Pacific
First of all, in the cloud computer room and data center application areas, through the close cooperation between large multinational search engines and social media giants, taking the mainland market as an example, Xilinx and Baidu (Baidu) search engine accelerators have important indicators. Sex, because machine learning uses the neural network model and the Training Framework to train algorithms on the high-performance computing (HPC) platform. Once the results are applied to the acceleration card design of the data center, it is often necessary to make details. The adjustment and development, so the flexibility of the logic design requirements, the FPGA's programmable ability is important.
Xilinx's solution quickly captures the attention of most large-scale users, relying on two important incentives, namely low power consumption and a highly flexible hardware platform, to provide data center-specific accelerator performance advantages and support data center operators. To create faster and more innovative applications and technologies.
Xilinx introduces Reconfigurable Accelera Accon Stack technology for three emerging computing-intensive applications such as machine learning, big data analytics and video streaming (Video Streaming), with a variety of application link libraries, development tools and The reference design effectively reduces the design logic of design engineers to develop FPGAs, quickly solves the challenges of rapid growth of workloads in data centers, and accelerates the launch of cloud service products.
The second important application area is the deployment of edge computing devices. Due to the use of a large number of video lenses, Embedded Vision technology can bring more intelligent applications because of the use of machine learning algorithms. .
However, embedded systems consider power consumption requirements and cannot use 32- or 64-bit high-performance processors. Therefore, a technology has been developed that allows most of the 32-bit or 16-bit floating-point operations (FloaTIng Point) to be used. The algorithm can be placed on the intelligent terminal device of the 8-bit integer arithmetic processor. The Xilinx solution maintains the accuracy of the algorithm on the embedded system in such applications, so the algorithm obtained by using machine learning is used. When deployed to systems with low power consumption and low computational density, artificial intelligence applications can still be completed.
Intelligent terminal computing applications with the development of air-to-air and self-driving cars, especially automatic identification, route planning in advanced driver assistance systems (ADAS), and automatic avoidance of air obstacles on drones, or video surveillance and identification analysis Emerging features, leading highly anticipated applications, also boosted the market share of FPGAs.
Another important point is the application of Industry 4.0, which uses Sensor Fusion devices to linearly plan and classify control data collected in industrial fields to capture the current state of industrial equipment. Choosing Xilinx's FPGA components provides a wide range of high-speed interfaces on a single chip, enabling higher performance at lower cost, and saving development resources because various development environments are integrated on the same platform, allowing customers to focus on developing differentiated functions Products, get market opportunities.
Low power consumption and fast response speed Compared to FPGA -based solutions and GPU-based solutions, Luo Lin analyzes the main advantages of FPGAs, the first low-power design, usually 3 watts of power is designed to support the entire acceleration The second important advantage of the card's performance is the fast response speed and the response speed in microseconds. It is especially suitable for the field of air-to-air or self-driving applications, because the reaction speed determines the important appeal of personal safety.
But FPGAs also have weaknesses. Luo Lin is flat, that is, engineers have a long time to develop FPGAs, because they need to be familiar with EDA tools such as logic line analysis and place and route to design FPGA lines, although FPGAs can now also use OpenCL and C languages. Programming in a software-like process, but undoubtedly, its engineering design threshold is still higher than the GPU design. Luo Lin emphasizes that Xilinx forms a developer community for FPGA in machine learning and develops rich tools and links. The library has already greatly improved the design efficiency.
Luo Lin pointed out that Xilinx's FPGA solution, which has been used as the main communication system in the world's major communication system rooms, utilizes intensive computing applications as a signal processing and application in communication standards, accounting for almost Xilinx. 50% of the main source of income, but with the application of data center accelerator card and edge computing terminal led by machine learning, covering more unmanned aerial cameras, intelligent video image monitoring systems, self-driving applications, Contributing to Xilinx's other 40% revenue source, the outlook is very explosive.
Looking forward to 2017's goal, first, Xilinx sets the application of Industrial Internet of Things (IIoT), from the PLC applications commonly used in industrial equipment, and the integration of industrial communication protocols through IEEE TSN and OPC-UA to grasp real-time information. Develop industrial applications. Second, face the enhancement of machine learning algorithms and related FPGA design tools, and accelerate the rapid deployment of artificial intelligence functions to achieve better performance requirements and reduce development costs.
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