IoT based Performance Improvement of Single Instruction Multiple Data (SIMD) Processor Array for Wireless Sensor Networks Application.
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| Title: | IoT based Performance Improvement of Single Instruction Multiple Data (SIMD) Processor Array for Wireless Sensor Networks Application. |
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| Authors: | VELLIANGIRI, A.1 velliangiria@gmail.com, KALIMUTHU, VINOTH KUMAR2 vinodkumaran87@gmail.com, BALAJI, C. G.3 cgbalaji@sidtm.edu.in, A., MOHAN KUMAR4 mohanarivumani94@gmail.com |
| Source: | Technical Gazette / Tehnički Vjesnik. 2025, Vol. 32 Issue 1, p66-71. 6p. |
| Subjects: | SIMD (Computer architecture), Verilog (Computer hardware description language), Field programmable gate arrays, Wireless sensor networks, Array processors |
| Abstract: | The advent of the Internet of Things (IoT) has brought about the need for more sophisticated and low-cost resource devices compared to traditional embedded systems. These IoT devices, which are often deployed in wireless sensor networks, must operate within strict power constraints. To meet these requirements and to harness the potential of IoT, power consumption planning strategies have evolved, necessitating the integration of new capabilities, including machine learning. In this research, the focus is on migrating machine learning applications onto Field Programmable Gate Arrays (FPGAs) for IoT edge devices, specifically targeting wireless sensor network applications. FPGAs offer flexibility and parallel processing capabilities, making them well-suited for IoT applications where machine learning is increasingly important. A critical component in this work is the development of a Single Instruction Multiple Data (SIMD) processor array, optimized for FPGA implementation. SIMD architectures allow for parallel processing of data, which is essential for efficient machine learning tasks. The research also includes the design and implementation of a multiplier-accumulator (MAC) unit within the SIMD processor array, and an innovative approach is employed using the Dual Field Vedic multiplier. Notably, the researchers opt for the Vedic multiplier design over traditional Booth's method due to its advantages in terms of reducing latency and hardware complexity. The Vedic multiplier, which draws inspiration from ancient Indian mathematics, offers potential performance gains in this context. The research methodology involves creating a high-performance SIMD processor array using FPGA technology and programming it using the Verilog hardware description language. Through FPGA experimentation and analysis, the researchers gather data on various performance metrics. These include area overhead, time delay details, and power consumption parameters. The primary goal of this research is to demonstrate the advantages of the Vedic multiplier in the context of the SIMD system, highlighting its potential to enhance the efficiency and effectiveness of machine learning applications on FPGA-based IoT edge devices. By comparing the results obtained with the Vedic multiplier to those of conventional dual-field multipliers, the research aims to provide valuable insights into the feasibility and benefits of this approach for wireless sensor networks and other IoT applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 182371829 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: IoT based Performance Improvement of Single Instruction Multiple Data (SIMD) Processor Array for Wireless Sensor Networks Application. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22VELLIANGIRI%2C+A%2E%22">VELLIANGIRI, A.</searchLink><relatesTo>1</relatesTo><i> velliangiria@gmail.com</i><br /><searchLink fieldCode="AR" term="%22KALIMUTHU%2C+VINOTH+KUMAR%22">KALIMUTHU, VINOTH KUMAR</searchLink><relatesTo>2</relatesTo><i> vinodkumaran87@gmail.com</i><br /><searchLink fieldCode="AR" term="%22BALAJI%2C+C%2E+G%2E%22">BALAJI, C. G.</searchLink><relatesTo>3</relatesTo><i> cgbalaji@sidtm.edu.in</i><br /><searchLink fieldCode="AR" term="%22A%2E%2C+MOHAN+KUMAR%22">A., MOHAN KUMAR</searchLink><relatesTo>4</relatesTo><i> mohanarivumani94@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2025, Vol. 32 Issue 1, p66-71. 6p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22SIMD+%28Computer+architecture%29%22">SIMD (Computer architecture)</searchLink><br /><searchLink fieldCode="DE" term="%22Verilog+%28Computer+hardware+description+language%29%22">Verilog (Computer hardware description language)</searchLink><br /><searchLink fieldCode="DE" term="%22Field+programmable+gate+arrays%22">Field programmable gate arrays</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+networks%22">Wireless sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Array+processors%22">Array processors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The advent of the Internet of Things (IoT) has brought about the need for more sophisticated and low-cost resource devices compared to traditional embedded systems. These IoT devices, which are often deployed in wireless sensor networks, must operate within strict power constraints. To meet these requirements and to harness the potential of IoT, power consumption planning strategies have evolved, necessitating the integration of new capabilities, including machine learning. In this research, the focus is on migrating machine learning applications onto Field Programmable Gate Arrays (FPGAs) for IoT edge devices, specifically targeting wireless sensor network applications. FPGAs offer flexibility and parallel processing capabilities, making them well-suited for IoT applications where machine learning is increasingly important. A critical component in this work is the development of a Single Instruction Multiple Data (SIMD) processor array, optimized for FPGA implementation. SIMD architectures allow for parallel processing of data, which is essential for efficient machine learning tasks. The research also includes the design and implementation of a multiplier-accumulator (MAC) unit within the SIMD processor array, and an innovative approach is employed using the Dual Field Vedic multiplier. Notably, the researchers opt for the Vedic multiplier design over traditional Booth's method due to its advantages in terms of reducing latency and hardware complexity. The Vedic multiplier, which draws inspiration from ancient Indian mathematics, offers potential performance gains in this context. The research methodology involves creating a high-performance SIMD processor array using FPGA technology and programming it using the Verilog hardware description language. Through FPGA experimentation and analysis, the researchers gather data on various performance metrics. These include area overhead, time delay details, and power consumption parameters. The primary goal of this research is to demonstrate the advantages of the Vedic multiplier in the context of the SIMD system, highlighting its potential to enhance the efficiency and effectiveness of machine learning applications on FPGA-based IoT edge devices. By comparing the results obtained with the Vedic multiplier to those of conventional dual-field multipliers, the research aims to provide valuable insights into the feasibility and benefits of this approach for wireless sensor networks and other IoT applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20231001000978 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 66 Subjects: – SubjectFull: SIMD (Computer architecture) Type: general – SubjectFull: Verilog (Computer hardware description language) Type: general – SubjectFull: Field programmable gate arrays Type: general – SubjectFull: Wireless sensor networks Type: general – SubjectFull: Array processors Type: general Titles: – TitleFull: IoT based Performance Improvement of Single Instruction Multiple Data (SIMD) Processor Array for Wireless Sensor Networks Application. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: VELLIANGIRI, A. – PersonEntity: Name: NameFull: KALIMUTHU, VINOTH KUMAR – PersonEntity: Name: NameFull: BALAJI, C. G. – PersonEntity: Name: NameFull: A., MOHAN KUMAR IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 32 – Type: issue Value: 1 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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