ITM Web Conf.
Volume 52, 2023International Conference on Connected Object and Artificial Intelligence (COCIA’2023)
|Number of page(s)||9|
|Section||Artificial Intelligence and its Application|
|Published online||08 May 2023|
Implementing Convolutional Neural Networks on FPGA: A Survey and Research
1 LabTIC, ENSA, Abdelmalek Essaadi University, Tangier, Morocco
2 TIM Team, ENSA, Cadi Ayyad University, Marrakech, Morocco
3 Universidad Politécnica de Cartagena, Cartagena, Spain
* Corresponding author: Haijoub.Abdel@gmail.com
The implementation of CNN FPGA is of increasing importance due to the growing demand for low-power and high-performance edge AI applications. This paper presents a comprehensive survey and research on the topic, with a focus on comparing and evaluating the performance of two main FPGA architectures, streaming and single unit computing. The study includes a detailed evaluation of the state-of-the-art CNNs, LeNet-5 and YOLOv2, on both FPGA architectures. The results provide useful insights into the trade-offs involved, limitations, challenges, and the complexity of implementing CNNs on FPGAs. The paper highlights the difficulties and intricacies involved in implementing CNNs on FPGAs and provides potential solutions for improving performance and efficiency.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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