FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917603291693056 |
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| author | Toupas, Petros Bouganis, Christos-Savvas Tzovaras, Dimitrios |
| author_facet | Toupas, Petros Bouganis, Christos-Savvas Tzovaras, Dimitrios |
| contents | 3D Convolutional Neural Networks are gaining increasing attention from researchers and practitioners and have found applications in many domains, such as surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval. However, their widespread adoption is hindered by their high computational and memory requirements, especially when resource-constrained systems are targeted. This paper addresses the problem of mapping X3D, a state-of-the-art model in Human Action Recognition that achieves accuracy of 95.5\% in the UCF101 benchmark, onto any FPGA device. The proposed toolflow generates an optimised stream-based hardware system, taking into account the available resources and off-chip memory characteristics of the FPGA device. The generated designs push further the current performance-accuracy pareto front, and enable for the first time the targeting of such complex model architectures for the Human Action Recognition task. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_18479 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition Toupas, Petros Bouganis, Christos-Savvas Tzovaras, Dimitrios Computer Vision and Pattern Recognition Artificial Intelligence Hardware Architecture Machine Learning 3D Convolutional Neural Networks are gaining increasing attention from researchers and practitioners and have found applications in many domains, such as surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval. However, their widespread adoption is hindered by their high computational and memory requirements, especially when resource-constrained systems are targeted. This paper addresses the problem of mapping X3D, a state-of-the-art model in Human Action Recognition that achieves accuracy of 95.5\% in the UCF101 benchmark, onto any FPGA device. The proposed toolflow generates an optimised stream-based hardware system, taking into account the available resources and off-chip memory characteristics of the FPGA device. The generated designs push further the current performance-accuracy pareto front, and enable for the first time the targeting of such complex model architectures for the Human Action Recognition task. |
| title | FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2305.18479 |