基于CNTFET的模式识别电路与CMOS技术的比较研究

S. Archana, B. Madhavi, I. V. Murlikrishna
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引用次数: 0

摘要

人工智能是基于数学方程和人工神经元的神经网络的组成部分。本文的重点是在32nm技术中使用CNTFET模型实现3位模式识别。采用本体CMOS技术对其进行分析表明,基于CNTFET的设计在功耗、延迟和功率延迟产品方面具有更好的性能。本文旨在分析神经网络方法在模式识别中的应用。对逆变器、电流镜和电流模式汉明神经网络(HNN)的3位模式识别进行了HSPICE 49级仿真。利用nano_model_39 CNFET库对3位模式识别电路进行了瞬态和直流仿真,结果表明该电路的功耗不变,但传输延迟降低了40%。因此,使用CNTFET可将功率延迟产品提高100倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study of CNTFET Based Pattern Recognition Circuits in Comparison With CMOS Technology
Artificial intelligence an integral part of a neural network is based on mathematical equations and artificial neurons. The focus here is to implement 3 bit pattern recognition using CNTFET model in 32nm technology. Its analysis with bulk CMOS technology shows CNTFET based design has better performance in terms of power dissipation, delay and power delay product. This paper aims at analyzing neural network method in pattern recognition. HSPICE level 49 simulations of inverter, current mirror and current mode hamming neural network (HNN) for 3 bit pattern recognition is done. The transient and DC simulation of 3 bit pattern recognition circuit using nano_model_39 CNFET library shows power dissipation same but propagation delays is reduced by 40%. Hence power delay product is improved 100 times using CNTFET.
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