Convolutional Neural Networks using FPGA-based Pipelining




Convolutional Neural Networks, FPGA, Pipelining, Accelerators, Hardware, Performance


In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of the use of FPGA-based pipelining for hardware acceleration of CNNs. These days, most people use convolutional neural networks (CNNs) to perform computer vision tasks like picture categorization and object recognition. The processing and memory demands of CNNs, however, can be excessive, especially for real-time applications. In order to speed up CNNs, FPGA-based pipelining has emerged as a viable option thanks to its parallel processing capabilities and low power consumption. The examination describes the fundamentals of FPGA-based pipelining and the basic structure of convolutional neural networks (CNNs). The current best practises for developing pipelined accelerators for CNNs on FPGAs are then reviewed, covering topics like partitioning and pipelining. Area and power limits, memory needs, and latency considerations are only some of the difficulties and trade-offs discussed in the article. In addition, the survey evaluates and contrasts the various pipelined FPGA accelerators for CNNs in terms of performance, energy consumption, and resource utilisation. Future directions and potential research areas are also discussed in the paper, such as the use of approximate computing techniques, the integration of reconfigurable architectures with emerging memory technologies, and the exploration of hybrid architectures that combine FPGAs and other hardware accelerators. This survey was created to aid researchers and practitioners in developing efficient and effective hardware accelerators for neural networks by providing a thorough overview of current trends and issues in FPGA-based pipelining for CNNs.


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How to Cite

G. A. . Ali and ahmed hussein ali, “Convolutional Neural Networks using FPGA-based Pipelining”, Iraqi Journal For Computer Science and Mathematics, vol. 4, no. 2, pp. 215–223, May 2023.
DOI: 10.52866/ijcsm.2023.02.02.019
Published: 2023-05-23