Analysis and application on enhancing CNN performance via FPGA integration

Yihang Zhu

doi:10.1117/12.3052318

11 December 2024 Analysis and application on enhancing CNN performance via FPGA integration

Yihang Zhu

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 1344523 (2024) https://doi.org/10.1117/12.3052318
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yihang Zhu "Analysis and application on enhancing CNN performance via FPGA integration", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 1344523 (11 December 2024); https://doi.org/10.1117/12.3052318

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KEYWORDS

Field programmable gate arrays

Convolutional neural networks

Power consumption

Convolution

Digital signal processing

Image processing

Computer hardware

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