首页> 外文会议>International Work-Conference on Artificial Neural Networks(IWANN 2005); 20050608-10; Barcelona(ES) >Ultra Low-Power Neural Inspired Addition: When Serial Might Outperform Parallel Architectures
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Ultra Low-Power Neural Inspired Addition: When Serial Might Outperform Parallel Architectures

机译:超低功耗神经启发的附加产品:当串行性能优于并行架构时

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In this paper we analyse a serial (ripple carry) and a parallel (Kogge-Stone) adder when operating in subthreshold at 100nm and 70nm. These are targeted for ultra low power consumption applications. The elementary gates used are threshold logic gates (perceptrons). Simulations have been performed both with and without considering the delay on the wires. These simulations confirm that wires play a significant role, reducing the speed advantage of the parallel adder (over the serial one) from 4.5x to 2.2-2.4x. A promising result is that the speed of both adders improves more than 10x when migrating from 100nm to 70nm. The full adder based on threshold logic gates (used in the ripple carry adder) improves on previously known full adders, achieving 1.6fJ when operated at 200mV in 120nm CMOS. Finally, the speed of the parallel adder can be matched by the serial adder when operating at only 10-20% higher V_(dd), while still requiring less power and energy.
机译:在本文中,我们分析了在100nm和70nm的亚阈值下工作时的串行(纹波进位)和并行(Kogge-Stone)加法器。这些是针对超低功耗应用的。所使用的基本门是阈值逻辑门(感知器)。在考虑和不考虑导线延迟的情况下都进行了仿真。这些仿真证实,导线发挥了重要作用,将并行加法器(相对于串行加法器)的速度优势从4.5倍降低到2.2-2.4倍。一个有希望的结果是,当从100nm迁移到70nm时,两个加法器的速度提高了10倍以上。基于阈值逻辑门的全加法器(在纹波进位加法器中使用)在先前已知的全加法器上有所改进,在120nm CMOS中以200mV工作时达到1.6fJ。最后,当以仅高10%至20%的V_(dd)工作时,并行加法器的速度可以与串行加法器相匹配,同时仍需要较少的功率和能量。

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