FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology
Fuzzy C-Means (FCM) is a widely used clustering algorithm that performs well in various scientific applications. Implementing FCM involves a massive number of computations, and many parallelization techniques based on GPUs and multicore systems have been suggested. In this study, we present a method...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/8260283 |
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| _version_ | 1832565764088922112 |
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| author | Abedalmuhdi Almomany Amin Jarrah Anwar Al Assaf |
| author_facet | Abedalmuhdi Almomany Amin Jarrah Anwar Al Assaf |
| author_sort | Abedalmuhdi Almomany |
| collection | DOAJ |
| description | Fuzzy C-Means (FCM) is a widely used clustering algorithm that performs well in various scientific applications. Implementing FCM involves a massive number of computations, and many parallelization techniques based on GPUs and multicore systems have been suggested. In this study, we present a method for optimizing the FCM algorithm for high-speed field-programmable gate technology (FPGA) using a high-level C-like programming language called open computing language (OpenCL). The method was designed to enable the high-level compiler/synthesis tool to manipulate a task-parallelism model and create an efficient design. Our experimental results (based on several datasets) show that the proposed method makes the FCM execution time more than 186 times faster than the conventional design running on a single-core CPU platform. Also, its processing power reached 89 giga floating points operations per second (GFLOPs). |
| format | Article |
| id | doaj-art-14f6994ab47f451dae8cc166ddde1af4 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-14f6994ab47f451dae8cc166ddde1af42025-02-03T01:06:38ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/8260283FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA TechnologyAbedalmuhdi Almomany0Amin Jarrah1Anwar Al Assaf2Department of Computer EngineeringDepartment of Computer EngineeringAviation Sciences Dean/AMMAN Arab UniversityFuzzy C-Means (FCM) is a widely used clustering algorithm that performs well in various scientific applications. Implementing FCM involves a massive number of computations, and many parallelization techniques based on GPUs and multicore systems have been suggested. In this study, we present a method for optimizing the FCM algorithm for high-speed field-programmable gate technology (FPGA) using a high-level C-like programming language called open computing language (OpenCL). The method was designed to enable the high-level compiler/synthesis tool to manipulate a task-parallelism model and create an efficient design. Our experimental results (based on several datasets) show that the proposed method makes the FCM execution time more than 186 times faster than the conventional design running on a single-core CPU platform. Also, its processing power reached 89 giga floating points operations per second (GFLOPs).http://dx.doi.org/10.1155/2022/8260283 |
| spellingShingle | Abedalmuhdi Almomany Amin Jarrah Anwar Al Assaf FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology Journal of Electrical and Computer Engineering |
| title | FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology |
| title_full | FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology |
| title_fullStr | FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology |
| title_full_unstemmed | FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology |
| title_short | FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology |
| title_sort | fcm clustering approach optimization using parallel high speed intel fpga technology |
| url | http://dx.doi.org/10.1155/2022/8260283 |
| work_keys_str_mv | AT abedalmuhdialmomany fcmclusteringapproachoptimizationusingparallelhighspeedintelfpgatechnology AT aminjarrah fcmclusteringapproachoptimizationusingparallelhighspeedintelfpgatechnology AT anwaralassaf fcmclusteringapproachoptimizationusingparallelhighspeedintelfpgatechnology |