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JAIT 2023 Vol.14(3): 479-487
doi: 10.12720/jait.14.3.479-487

Efficient FPGA-Based Convolutional Neural Network Implementation for Edge Computing

Cuong Pham-Quoc 1,2,* and Tran Ngoc Thinh 1,2
1. Department of Computer Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam
2. Department of Computer Engineering, Vietnam National University, Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, Vietnam
*Correspondence: cuongpham@hcmut.edu.vn (C.P.-Q.)

Manuscript received January 16, 2023; revised March 2, 2023; accepted April 5, 2023; published May 24, 2023.

Abstract—In recent years, accelerating convolutional neural networks on Field Programmable Gate Array (FPGA) to improve the performance of the inference phase of artificial intelligent edge computing applications is a promising approach. This paper presents our proposed architecture for building a convolution neural network acceleration core on FPGA. The proposed FPGA-based core targets edge computing platforms where hardware resources and power efficiency are essential requirements. We use the MobileNet neural network model for image classification as a case study to evaluate our proposed system. We compare our work with a quad-core ARM Cortex processor at 1.2GHz and achieve speed-ups by up to 14.77× convolution operators. Although our system is worse than a 6-core Intel Core i7 processor, it is more energy-efficiency than the Intel processor. Our proposed system is the best fit for edge computing.
Keywords—Field Programmable Gate Array (FPGA), convolutional neural network, hardware accelerator, MobileNet

Cite: Cuong Pham-Quoc and Tran Ngoc Thinh, "Efficient FPGA-Based Convolutional Neural Network Implementation for Edge Computing," Journal of Advances in Information Technology, Vol. 14, No. 3, pp. 479-487, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.