Analysis and application on enhancing CNN performance via FPGA integration

In the rapidly advancing realm of information processing, the intersection of Field Programmable Gate Array (FPGA) technology and Convolutional Neural Networks (CNNs) has garnered considerable attention. This paper presents a comprehensive review of the evolution of FPGAs and CNNs, delving into the architectural intricacies of FPGAs and the compositional elements of CNNs, encompassing the convolutional layer, pooling layer, and fully connected layer. A particular emphasis is placed on analyzing the principles and functionality of the pipeline structure in FPGAs, elucidating its pivotal role in expediting the CNN inference process. The integration of pipeline architecture within FPGAs is a strategic design choice aimed at enhancing data processing efficiency. This structure allows for the execution of instructions across multiple stages concurrently, significantly boosting computational speed, a feature particularly advantageous for the extensive matrix operations characteristic of convolutional neural networks. Concurrently, CNNs have emerged as a powerhouse in deep learning, demonstrating remarkable success in image recognition and speech processing domains. These networks function by extracting features through sequential layer processing and executing classifications in the fully connected layer. When juxtaposed with traditional CPUs and GPUs, FPGAs reveal distinct benefits in CNN applications. The inherent programmability of FPGAs permits extensive customization, aligning seamlessly with diverse neural network architectures and achieving superior energy efficiency. However, it's crucial to note that CPUs and GPUs maintain certain advantages in general-purpose computing, particularly in handling complex logic and extensive data sets.