With the rapid development of artificial intelligence (AI) technology, the demand for computing performance is also growing. FPGA (Field Programmable Gate Array), as a high-performance, low-power, flexible programming hardware platform, is gradually emerging in the field of AI, showing unique advantages, and providing strong support for the landing of AI applications.

First, the application of FPGA in deep learning deep learning is an important branch of AI, which involves massive data operations, FPGA can optimize the hardware acceleration for the core operations such as convolution and pooling in deep learning algorithms. For example, in the image recognition task, deploying the convolutional neural network on FPGA significantly shortens the inference time by processing multiple pixel data in parallel, making real-time image recognition possible, which is of great significance in the field of security monitoring and can quickly and accurately identify abnormal people and behaviors to protect public safety. In addition, in the field of medical image diagnosis, for example, when identifying lesions in X-rays, CT images, etc., FPGA-accelerated deep learning models can quickly analyze the image features, assisting doctors to more efficiently and accurately determine the condition, and lesions such as small tumors in the early stages of the disease can be detected in time, fighting for patients‘ valuable treatment time. FPGA helps natural language processing Natural language processing also faces high complexity computing challenges, FPGA can accelerate the text processing process, such as word vector generation, parsing and other key aspects. In machine translation scenarios, FPGAs, with their efficient computing architecture, can quickly process input text into the target language, which significantly improves translation efficiency compared with traditional CPU processing and meets the urgent demand for instant translation in globalized communication. Moreover, in terms of intelligent voice assistants, FPGAs can accelerate the processing of voice recognition to text and the understanding and analysis of semantics, enabling voice assistants to respond to user commands faster and more accurately, for example, when users ask about weather, check routes and other needs, they can instantly give accurate answers to enhance the user experience. Advantages of FPGA in the field of intelligent robots Intelligent robots need to perceive the surrounding environment in real time, make decisions quickly and execute actions. low-latency characteristics of FPGAs are crucial, which can quickly process images, sounds and other data from sensors, so that robots can instantly judge the road conditions, identify obstacles, and adjust the travel route in a timely manner; at the same time, in the implementation of complex motion control algorithms, the powerful computing power of FPGAs guarantees the smoothness of robot actions. At the same time, in the implementation of complex motion control algorithms, FPGA provides powerful algorithms to ensure the smoothness and accuracy of the robot‘s movements, so that it can better adapt to changing work scenarios, whether it is precision operations on industrial production lines, or flexible assistance in family services. Taking the sorting robot in logistics warehousing as an example, it needs to quickly identify goods of different shapes and sizes and accurately place them in the corresponding shelf positions. FPGA helps its visual recognition system operate efficiently and guarantees the precision of the robotic arm‘s motion control, which greatly improves the efficiency and accuracy of logistics sorting. With the rise of the Internet of Things, the demand for data processing at the edge is becoming more and more prominent. FPGAs are small and have low power consumption, which is suitable for deployment in edge devices, such as smart cameras, sensor nodes, etc. The data collected at the edge is directly processed by FPGAs. Preliminary AI analysis of the collected data is performed directly at the edge, and key information is filtered out and then uploaded to the cloud, which not only reduces the pressure on network bandwidth, but also ensures privacy, and guarantees the realization of low-latency and high-reliability local intelligent services in the fields of intelligent transportation, remote medical monitoring, and so on. In intelligent transportation, roadside smart cameras equipped with FPGAs can analyze real-time information such as license plate, speed, and model of passing vehicles, which plays a key role in monitoring traffic violations and traffic flow statistics, and can provide timely feedback of important data to the traffic management system to optimize traffic scheduling. In short, FPGA, with its excellent performance and flexible adaptability, is being deeply integrated into various fields of AI, complementing traditional computing chips, and jointly promoting AI to a new peak, and is expected to unlock more innovative application scenarios in the future, and continue to empower the progress of science and technology.