Accelerating FPGA-surf feature detection module by memory access reduction
Feature detection is an important concept in the area of image processing to compute image abstractions of image information, which is used for image recognition and many other applications. One of the popular algorithm used is called the Speeded-Up Robust Features (SURF), which realized the scale s...
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| Format: | Article |
| Language: | English |
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University of Malaya
2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/27574/1/Accelerating%20FPGA-SURF%20feature%20detection%20module%20by%20memory%20access%20reduction.pdf |
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| author | Mohd. Yamani Idna, Idris Nor Bakiah, Abd. Warif Hamzah, Arof Noorzaily, Mohamed Noor Ainuddin Wahid, Abdul Wahab Zaidi, Razak |
| author_facet | Mohd. Yamani Idna, Idris Nor Bakiah, Abd. Warif Hamzah, Arof Noorzaily, Mohamed Noor Ainuddin Wahid, Abdul Wahab Zaidi, Razak |
| author_sort | Mohd. Yamani Idna, Idris |
| collection | UMP |
| description | Feature detection is an important concept in the area of image processing to compute image abstractions of image information, which is used for image recognition and many other applications. One of the popular algorithm used is called the Speeded-Up Robust Features (SURF), which realized the scale space pyramid to detect the features. For this reason, prior researchers concentrate on applying parallelism onto the SURF multiple layers using technology such as Field Programmable Gate Array (FPGA). However, prior FPGA-SURF implementation does not emphasis on memory access limitation that can affect the overall performance of a system. This paper proposes a study on FPGA-SURF and memory access implementation in feature detection area. We conduct a profiling test and founds that the external memory access to fetch the integral image data in SURF highly affects the overall performance. We also found that the SURF algorithm memory access has redundant repeating pattern that can be reduced. Therefore, a controller design that stores repeating data (for the subsequent process) in an on-chip memory is proposed. This method reduces the external memory access and can increase the overall performance. The result shows that our proposed method improves the existing method (i.e. without the memory access reduction) by 1.23 times when the external memory latency is 20ns. |
| first_indexed | 2024-03-06T12:40:23Z |
| format | Article |
| id | UMPir27574 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:40:23Z |
| publishDate | 2019 |
| publisher | University of Malaya |
| record_format | dspace |
| spelling | UMPir275742020-04-03T07:00:16Z http://umpir.ump.edu.my/id/eprint/27574/ Accelerating FPGA-surf feature detection module by memory access reduction Mohd. Yamani Idna, Idris Nor Bakiah, Abd. Warif Hamzah, Arof Noorzaily, Mohamed Noor Ainuddin Wahid, Abdul Wahab Zaidi, Razak QA76 Computer software TA Engineering (General). Civil engineering (General) Feature detection is an important concept in the area of image processing to compute image abstractions of image information, which is used for image recognition and many other applications. One of the popular algorithm used is called the Speeded-Up Robust Features (SURF), which realized the scale space pyramid to detect the features. For this reason, prior researchers concentrate on applying parallelism onto the SURF multiple layers using technology such as Field Programmable Gate Array (FPGA). However, prior FPGA-SURF implementation does not emphasis on memory access limitation that can affect the overall performance of a system. This paper proposes a study on FPGA-SURF and memory access implementation in feature detection area. We conduct a profiling test and founds that the external memory access to fetch the integral image data in SURF highly affects the overall performance. We also found that the SURF algorithm memory access has redundant repeating pattern that can be reduced. Therefore, a controller design that stores repeating data (for the subsequent process) in an on-chip memory is proposed. This method reduces the external memory access and can increase the overall performance. The result shows that our proposed method improves the existing method (i.e. without the memory access reduction) by 1.23 times when the external memory latency is 20ns. University of Malaya 2019-01-31 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27574/1/Accelerating%20FPGA-SURF%20feature%20detection%20module%20by%20memory%20access%20reduction.pdf Mohd. Yamani Idna, Idris and Nor Bakiah, Abd. Warif and Hamzah, Arof and Noorzaily, Mohamed Noor and Ainuddin Wahid, Abdul Wahab and Zaidi, Razak (2019) Accelerating FPGA-surf feature detection module by memory access reduction. Malaysian Journal of Computer Science, 32 (1). pp. 47-61. ISSN 0127-9084. (Published) https://doi.org/10.22452/mjcs.vol32no1.4 https://doi.org/10.22452/mjcs.vol32no1.4 |
| spellingShingle | QA76 Computer software TA Engineering (General). Civil engineering (General) Mohd. Yamani Idna, Idris Nor Bakiah, Abd. Warif Hamzah, Arof Noorzaily, Mohamed Noor Ainuddin Wahid, Abdul Wahab Zaidi, Razak Accelerating FPGA-surf feature detection module by memory access reduction |
| title | Accelerating FPGA-surf feature detection module by memory access reduction |
| title_full | Accelerating FPGA-surf feature detection module by memory access reduction |
| title_fullStr | Accelerating FPGA-surf feature detection module by memory access reduction |
| title_full_unstemmed | Accelerating FPGA-surf feature detection module by memory access reduction |
| title_short | Accelerating FPGA-surf feature detection module by memory access reduction |
| title_sort | accelerating fpga surf feature detection module by memory access reduction |
| topic | QA76 Computer software TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/27574/1/Accelerating%20FPGA-SURF%20feature%20detection%20module%20by%20memory%20access%20reduction.pdf |
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