Research on the Application of Hardware Accelerators in Artificial Intelligence Systems

Abstract

Traditional general-purpose processors are constrained by operational efficiency when handling computationally intensive tasks such as deep learning, convolutional neural networks, and recurrent neural networks. Hardware accelerators, as specialized computing architectures, significantly outperform general-purpose processors in parallel computing, data throughput, and operational efficiency. Therefore, hardware accelerators have become one of the key factors driving the advancement of AI. This article systematically studies the basic principles of hardware accelerators, their main types (GPU, FPGA, ASIC), as well as the design and implementation methods of hardware accelerators. By comparing the performance metrics, power consumption characteristics and application adaptability of different accelerator architectures, the advantages of hardware accelerators over other accelerators are demonstrated. The paper also investigates the advantages and challenges of hardware accelerators in accelerating deep learning inference, training, and edge computing. According to the research results, accelerators tailored for specific AI tasks can significantly reduce latency and improve energy efficiency, and have broad application prospects in scenarios such as 5G, autonomous driving, and intelligent manufacturing. This research provides a reference for the designers of artificial intelligence systems to select and optimize hardware acceleration solutions, and also offers a direction for the future innovation of accelerator architectures.