Dynamic algorithm transformations (DAT) for low-power adaptive signal processing

M. Goel, Naresh R Shanbhag
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引用次数: 13

Abstract

Presented in this paper are algorithm transformation techniques for adaptive signal processing, which allow dynamic alteration of algorithm properties in response to signal non-stationarities. These transformations, referred to as dynamic algorithm transformations (DAT), jointly optimize algorithm and circuit performance measures such as signal-to-noise ratios (SNR) and power dissipation (P/sub D/), respectively. A DAT-based signal processing system is composed of a signal monitoring algorithm (SMA) block and a signal processing algorithm (SPA) block. First, computation of the theoretical power-optimum SPA configuration incorporating signal transition activity is presented. Next, practical SMA schemes are developed, which achieved power reduction by a combination of powering down the filter taps and modifying the coefficients. The DAT-based adaptive filter is then employed as a near-end cross-talk (NEXT) canceller in 155.52 Mb/s ATM-LAN over category 3 wiring. Simulation results indicate that the power savings for the NEXT canceller range from 21%-62% as the cable length varies from 100 meters to 70 meters.
动态算法变换(DAT)用于低功耗自适应信号处理
本文提出了自适应信号处理的算法转换技术,该技术允许动态改变算法特性以响应信号的非平稳性。这些转换被称为动态算法转换(DAT),分别优化算法和电路性能指标,如信噪比(SNR)和功耗(P/sub D/)。基于数据的信号处理系统由信号监测算法(SMA)模块和信号处理算法(SPA)模块组成。首先,给出了包含信号转移活度的理论功率最优SPA结构的计算。接下来,开发了实用的SMA方案,通过关闭滤波器抽头和修改系数的组合来实现功耗降低。基于数据的自适应滤波器随后被用作近端串扰(NEXT)消除器,在155.52 Mb/s的ATM-LAN中通过3类布线。仿真结果表明,当电缆长度从100米到70米变化时,NEXT消光器的节电范围为21% ~ 62%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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