{"title":"使用吠陀经文的吠陀乘法器与生物医学应用中现有乘法器的比较分析","authors":"R. Karthi Kumar , S.P. Vimal","doi":"10.1016/j.measen.2024.101302","DOIUrl":null,"url":null,"abstract":"<div><p>In today's computer world, a lot of real-time applications require rapid processing units. Arithmetic Logic Units (ALU) and Multiply-Accumulate (MAC) are the fundamental parts of these circuits and are necessary for their effective and rapid operation. The most significant prevalent part of digital signal processing devices is multipliers. The multiplier, adder, and registers need to be changed in order to maintain accuracy and increase execution speed, which will improve the performance of the ALU and MAC. The development of greater multipliers is being given priority for application in processors because of the increasing constraints on latency. To accelerate multiplication, it is essential to develop quicker multipliers. Vedic multipliers are preferred over different current expansions due to their low power consumption, fast operation, and efficient use of space. Vedic mathematics-based algorithms are often utilized to build quick, low-power multipliers. In addition to simulation results, this section covers the four sutras of Vedic mathematics: Urdhva Tiryakbhyam, Ekadhikena Purvena, Ekanyunena Purvena, and Nikhilam. Vedic multipliers are also compared to a variety of modern multipliers, including booth, Wallace, and array multipliers. All of the sutras are evaluated according to area, speed, power, propagation delay, and mean relative error (MRE) in the current research. The results of the study will be applied in the biomedical field.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"36 ","pages":"Article 101302"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002782/pdfft?md5=1b9cf104afb2938af8b3a7e22033f9a0&pid=1-s2.0-S2665917424002782-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Comparative analysis of Vedic multiplier using Vedic sutras with existing multipliers in biomedical application\",\"authors\":\"R. Karthi Kumar , S.P. Vimal\",\"doi\":\"10.1016/j.measen.2024.101302\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In today's computer world, a lot of real-time applications require rapid processing units. Arithmetic Logic Units (ALU) and Multiply-Accumulate (MAC) are the fundamental parts of these circuits and are necessary for their effective and rapid operation. The most significant prevalent part of digital signal processing devices is multipliers. The multiplier, adder, and registers need to be changed in order to maintain accuracy and increase execution speed, which will improve the performance of the ALU and MAC. The development of greater multipliers is being given priority for application in processors because of the increasing constraints on latency. To accelerate multiplication, it is essential to develop quicker multipliers. Vedic multipliers are preferred over different current expansions due to their low power consumption, fast operation, and efficient use of space. Vedic mathematics-based algorithms are often utilized to build quick, low-power multipliers. In addition to simulation results, this section covers the four sutras of Vedic mathematics: Urdhva Tiryakbhyam, Ekadhikena Purvena, Ekanyunena Purvena, and Nikhilam. Vedic multipliers are also compared to a variety of modern multipliers, including booth, Wallace, and array multipliers. All of the sutras are evaluated according to area, speed, power, propagation delay, and mean relative error (MRE) in the current research. The results of the study will be applied in the biomedical field.</p></div>\",\"PeriodicalId\":34311,\"journal\":{\"name\":\"Measurement Sensors\",\"volume\":\"36 \",\"pages\":\"Article 101302\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2665917424002782/pdfft?md5=1b9cf104afb2938af8b3a7e22033f9a0&pid=1-s2.0-S2665917424002782-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Measurement Sensors\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665917424002782\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement Sensors","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665917424002782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
摘要
在当今的计算机世界中,许多实时应用都需要快速处理单元。算术逻辑单元(ALU)和乘法累加器(MAC)是这些电路的基本组成部分,也是它们有效和快速运行的必要条件。乘法器是数字信号处理设备中最重要的组成部分。乘法器、加法器和寄存器需要改变,以保持精度并提高执行速度,从而改善 ALU 和 MAC 的性能。由于对延迟的限制越来越多,因此开发更大的乘法器在处理器中的应用成为当务之急。为了加速乘法运算,必须开发更快的乘法器。吠陀乘法器具有功耗低、运行速度快、空间利用率高等优点,是目前各种扩展乘法器的首选。基于吠陀数学的算法通常被用来构建快速、低功耗的乘法器。除仿真结果外,本节还介绍了吠陀数学的四部经文:Urdhva Tiryakbhyam、Ekadhikena Purvena、Ekanyunena Purvena 和 Nikhilam。此外,还将吠陀乘法器与各种现代乘法器进行了比较,包括展位乘法器、华莱士乘法器和阵列乘法器。目前的研究根据面积、速度、功率、传播延迟和平均相对误差 (MRE) 对所有乘法器进行了评估。研究结果将应用于生物医学领域。
Comparative analysis of Vedic multiplier using Vedic sutras with existing multipliers in biomedical application
In today's computer world, a lot of real-time applications require rapid processing units. Arithmetic Logic Units (ALU) and Multiply-Accumulate (MAC) are the fundamental parts of these circuits and are necessary for their effective and rapid operation. The most significant prevalent part of digital signal processing devices is multipliers. The multiplier, adder, and registers need to be changed in order to maintain accuracy and increase execution speed, which will improve the performance of the ALU and MAC. The development of greater multipliers is being given priority for application in processors because of the increasing constraints on latency. To accelerate multiplication, it is essential to develop quicker multipliers. Vedic multipliers are preferred over different current expansions due to their low power consumption, fast operation, and efficient use of space. Vedic mathematics-based algorithms are often utilized to build quick, low-power multipliers. In addition to simulation results, this section covers the four sutras of Vedic mathematics: Urdhva Tiryakbhyam, Ekadhikena Purvena, Ekanyunena Purvena, and Nikhilam. Vedic multipliers are also compared to a variety of modern multipliers, including booth, Wallace, and array multipliers. All of the sutras are evaluated according to area, speed, power, propagation delay, and mean relative error (MRE) in the current research. The results of the study will be applied in the biomedical field.