Hardware Implementation of Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have broad application prospects in computer vision, image processing, and other fields. However, their characteristics, such as high computational speed and large data volume, pose significant challenges for hardware implementation. This project aims to improve computational efficiency and reduce energy consumption by summarizing hardware-based convolutional neural network algorithms to meet real-time requirements. Additionally, it will summarize research methods for accelerating convolutional computation, pooling, and fully connected layers using different hardware platforms such as ASIC, FPGA, and GPU. This paper focuses on optimizing data flow, parallel processing, and memory architecture to reduce computation latency and energy consumption. To ensure network accuracy, methods such as quantization and pruning are employed to reduce model size and computational complexity. Literature indicates that custom hardware designs for convolutional neural networks significantly enhance performance and energy efficiency compared to traditional software implementations. This review aims to provide an efficient hardware acceleration method for practical deep learning algorithms and promote development in fields such as smart terminals and edge computing.