FPGA Correlator for Applications in Embedded Smart Devices
Correlation has a variety of applications that require signal processing. However, it is computationally intensive, and software correlators require high-performance processors for real-time data analysis. This is a challenge for embedded devices because of the limitation of computing resources. Har...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/12/4/236 |
| _version_ | 1827621474434809856 |
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| author | Christopher H. Moore Wei Lin |
| author_facet | Christopher H. Moore Wei Lin |
| author_sort | Christopher H. Moore |
| collection | DOAJ |
| description | Correlation has a variety of applications that require signal processing. However, it is computationally intensive, and software correlators require high-performance processors for real-time data analysis. This is a challenge for embedded devices because of the limitation of computing resources. Hardware correlators that use Field Programmable Gate Array (FPGA) technology can significantly boost computational power and bridge the gap between the need for high-performance computing and the limited processing power available in embedded devices. This paper presents a detailed FPGA-based correlator design at the register level along with the open-source Very High-Speed Integrated Circuit Hardware Description Language (VHDL) code. It includes base modules for linear and multi-tau correlators of varying sizes. Every module implements a simple and unified data interface for easy integration with standard and publicly available FPGA modules. Eighty-lag linear and multi-tau correlators were built for validation of the design. Three input data sets—constant signal, pulse signal, and sine signal—were used to test the accuracy of the correlators. The results from the FPGA correlators were compared against the outputs of equivalent software correlators and validated with the corresponding theoretical values. The FPGA correlators returned results identical to those from the software references for all tested data sets and were proven to be equivalent to their software counterparts. Their computation speed is at least 85,000 times faster than the software correlators running on a Xilinx MicroBlaze processor. The FPGA correlator can be easily implemented, especially on System on a Chip (SoC) integrated circuits that have processor cores and FPGA fabric. It is the ideal component for device-on-chip solutions in biosensing. |
| first_indexed | 2024-03-09T11:05:02Z |
| format | Article |
| id | doaj.art-4ef0abf0497b4ba78b00e8b1488cddae |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-09T11:05:02Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-4ef0abf0497b4ba78b00e8b1488cddae2023-12-01T00:58:21ZengMDPI AGBiosensors2079-63742022-04-0112423610.3390/bios12040236FPGA Correlator for Applications in Embedded Smart DevicesChristopher H. Moore0Wei Lin1Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USADepartment of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USACorrelation has a variety of applications that require signal processing. However, it is computationally intensive, and software correlators require high-performance processors for real-time data analysis. This is a challenge for embedded devices because of the limitation of computing resources. Hardware correlators that use Field Programmable Gate Array (FPGA) technology can significantly boost computational power and bridge the gap between the need for high-performance computing and the limited processing power available in embedded devices. This paper presents a detailed FPGA-based correlator design at the register level along with the open-source Very High-Speed Integrated Circuit Hardware Description Language (VHDL) code. It includes base modules for linear and multi-tau correlators of varying sizes. Every module implements a simple and unified data interface for easy integration with standard and publicly available FPGA modules. Eighty-lag linear and multi-tau correlators were built for validation of the design. Three input data sets—constant signal, pulse signal, and sine signal—were used to test the accuracy of the correlators. The results from the FPGA correlators were compared against the outputs of equivalent software correlators and validated with the corresponding theoretical values. The FPGA correlators returned results identical to those from the software references for all tested data sets and were proven to be equivalent to their software counterparts. Their computation speed is at least 85,000 times faster than the software correlators running on a Xilinx MicroBlaze processor. The FPGA correlator can be easily implemented, especially on System on a Chip (SoC) integrated circuits that have processor cores and FPGA fabric. It is the ideal component for device-on-chip solutions in biosensing.https://www.mdpi.com/2079-6374/12/4/236FPGA correlatordiffuse correlation spectroscopydynamic light scatteringfluorescence correlation spectroscopydevice-on-chip |
| spellingShingle | Christopher H. Moore Wei Lin FPGA Correlator for Applications in Embedded Smart Devices Biosensors FPGA correlator diffuse correlation spectroscopy dynamic light scattering fluorescence correlation spectroscopy device-on-chip |
| title | FPGA Correlator for Applications in Embedded Smart Devices |
| title_full | FPGA Correlator for Applications in Embedded Smart Devices |
| title_fullStr | FPGA Correlator for Applications in Embedded Smart Devices |
| title_full_unstemmed | FPGA Correlator for Applications in Embedded Smart Devices |
| title_short | FPGA Correlator for Applications in Embedded Smart Devices |
| title_sort | fpga correlator for applications in embedded smart devices |
| topic | FPGA correlator diffuse correlation spectroscopy dynamic light scattering fluorescence correlation spectroscopy device-on-chip |
| url | https://www.mdpi.com/2079-6374/12/4/236 |
| work_keys_str_mv | AT christopherhmoore fpgacorrelatorforapplicationsinembeddedsmartdevices AT weilin fpgacorrelatorforapplicationsinembeddedsmartdevices |