Research and Analysis of the Application of Artificial Intelligence Chips in the Field of Intelligent Recognition

Abstract

With the rapid development of deep learning technology, intelligent recognition technology, centered around convolutional neural networks (CNNs), has been widely applied in scenarios such as healthcare, image recognition, and autonomous driving. However, the training and inference processes of intelligent recognition models, especially deep CNNs, require massive computational and memory access efforts, posing severe performance and energy efficiency challenges to traditional computing architectures (such as CPUs and GPUs). As intelligent recognition technology becomes increasingly practical and widespread across society, performance requirements for CNNs are steadily increasing. This has significantly driven research into specialized artificial intelligence chips, enabling architectural innovation and optimization of intelligent recognition models.This article will review the early development and current status of intelligent recognition technology, the composition and evolution of convolutional neural networks (CNNs), which play a core role in recognition technology, and how AI chips optimize and enhance CNN performance.In summary, AI chips are specialized accelerators for intelligent recognition computing, and their development is mutually reinforcing with advances in deep learning algorithms. As model complexity continues to grow, AI chips designed for emerging architectures, supporting higher energy efficiency and hardware-software co-design, will continue to be a core driving force behind the advancement of intelligent recognition technology.