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A vedic mathematics based processor core for discrete wavelet transform using FinFET and CNTFET technology for biomedical signal processing

机译:基于离散数学的基于吠陀数学的处理器内核,使用FinFET和CNTFET技术进行生物医学信号处理

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This paper presents a new design for the implementation of Vedic mathematics based discrete wavelet transform for biomedical signal processing. The design of the low power architecture using alternate devices is the background of the work. The DWT architecture consists of the adder, multiplier, Multiply Accumulate (MAC) unit and RAM or ROM to store the co-efficients. The existing Complementary Metal Oxide Semiconductor based design suffers from leakage. The proposed FinFET and CNTFET technology will overcome the problem faced in CMOS technology. The processor core of the system on chip (SoC) designed using Vedic mathematics sutras. The efficiency of Vedic mathematics and advances of low power VLSI is combined in this paper. The CNTFET design reduces the power by about 95% and has controllability of the threshold voltage. The design is carried out in 32 nm FinFET technologyThe design is mainly focused on the complete Processor Core block implemented using a MAC with a Vedic multiplier using FinFET technology. The experiments were carried out using Synopsis HSpice. (C) 2019 Elsevier B.V. All rights reserved.
机译:本文提出了一种基于吠陀数学的离散小波变换用于生物医学信号处理的新设计。使用备用设备的低功耗架构设计是工作的背景。 DWT体系结构由加法器,乘法器,乘法累加(MAC)单元以及用于存储系数的RAM或ROM组成。现有的基于互补金属氧化物半导体的设计遭受泄漏。拟议的FinFET和CNTFET技术将克服CMOS技术面临的问题。使用吠陀数学经设计的片上系统(SoC)的处理器核心。本文结合了吠陀数学的效率和低功耗超大规模集成电路的先进性。 CNTFET设计可将功率降低约95%,并具有阈值电压的可控制性。该设计采用32 nm FinFET技术进行设计主要集中在使用MAC和采用FinFET技术的Vedic乘法器的完整处理器核心模块。实验使用Synopsis HSpice进行。 (C)2019 Elsevier B.V.保留所有权利。

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