FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery
Applications in computer vision and image analysis, including object recognition and diagnostic imaging, are reliant on a fundamental competency in image segmentation. However, high-computation methods are occasionally exceeded by the energy economy and processing speed of typical CPU-based systems....
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11083568/ |
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| author | Rupali Karthikeyan Deep Amit Lodaya Rama Muni Reddy Yanamala Rayappa David Amar Raj K. Krishna Prakasha T. Subeesh V. Anandkumar Archana Pallakonda |
| author_facet | Rupali Karthikeyan Deep Amit Lodaya Rama Muni Reddy Yanamala Rayappa David Amar Raj K. Krishna Prakasha T. Subeesh V. Anandkumar Archana Pallakonda |
| author_sort | Rupali Karthikeyan |
| collection | DOAJ |
| description | Applications in computer vision and image analysis, including object recognition and diagnostic imaging, are reliant on a fundamental competency in image segmentation. However, high-computation methods are occasionally exceeded by the energy economy and processing speed of typical CPU-based systems. To surpass these limitations, a hardware-accelerated picture segmentation method is introduced, leveraging the Alternating Direction Method of Multipliers (ADMM) technology, FPGA parallel processing, and sparse subset selection. ADMM algorithms are designed in high-level synthesis (HLS) C code for deployment on Xilinx Zynq UltraScale+ MPSoC. This approach simplifies hardware integration and maintains accuracy while reducing latency and improving energy efficiency. Significant energy savings and decreased execution times are indicated by experimental results, with FPGA achieving segmentation in 9 ms as opposed to 13 ms on a CPU, thereby proving the tremendous computational efficiency of FPGA-based solutions. These results demonstrate how hardware acceleration can enable scalable real-time applications in limited resources by overcoming computational constraints. |
| format | Article |
| id | doaj-art-0ffcd07a6ee6421e9ead30834248b237 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0ffcd07a6ee6421e9ead30834248b2372025-08-20T03:32:55ZengIEEEIEEE Access2169-35362025-01-011312824912826110.1109/ACCESS.2025.359016711083568FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution ImageryRupali Karthikeyan0https://orcid.org/0009-0005-1403-2729Deep Amit Lodaya1https://orcid.org/0009-0003-0943-8930Rama Muni Reddy Yanamala2https://orcid.org/0009-0007-9132-4914Rayappa David Amar Raj3https://orcid.org/0000-0002-5888-5513K. Krishna Prakasha4https://orcid.org/0000-0001-7797-1399T. Subeesh5V. Anandkumar6https://orcid.org/0000-0002-4533-9169Archana Pallakonda7https://orcid.org/0009-0005-7865-4156Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, IndiaAmrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing (IIITDM), Kancheepuram, Chennai, IndiaAmrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, IndiaSchool of Computer Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, IndiaAmrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, IndiaAmrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, Telangana, IndiaApplications in computer vision and image analysis, including object recognition and diagnostic imaging, are reliant on a fundamental competency in image segmentation. However, high-computation methods are occasionally exceeded by the energy economy and processing speed of typical CPU-based systems. To surpass these limitations, a hardware-accelerated picture segmentation method is introduced, leveraging the Alternating Direction Method of Multipliers (ADMM) technology, FPGA parallel processing, and sparse subset selection. ADMM algorithms are designed in high-level synthesis (HLS) C code for deployment on Xilinx Zynq UltraScale+ MPSoC. This approach simplifies hardware integration and maintains accuracy while reducing latency and improving energy efficiency. Significant energy savings and decreased execution times are indicated by experimental results, with FPGA achieving segmentation in 9 ms as opposed to 13 ms on a CPU, thereby proving the tremendous computational efficiency of FPGA-based solutions. These results demonstrate how hardware acceleration can enable scalable real-time applications in limited resources by overcoming computational constraints.https://ieeexplore.ieee.org/document/11083568/Aerial imagingcomputer visionimage segmentationoptimizationsparse subset selection |
| spellingShingle | Rupali Karthikeyan Deep Amit Lodaya Rama Muni Reddy Yanamala Rayappa David Amar Raj K. Krishna Prakasha T. Subeesh V. Anandkumar Archana Pallakonda FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery IEEE Access Aerial imaging computer vision image segmentation optimization sparse subset selection |
| title | FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery |
| title_full | FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery |
| title_fullStr | FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery |
| title_full_unstemmed | FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery |
| title_short | FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery |
| title_sort | fpga accelerated sparse subset segmentation using admm for high resolution imagery |
| topic | Aerial imaging computer vision image segmentation optimization sparse subset selection |
| url | https://ieeexplore.ieee.org/document/11083568/ |
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