Dynamic Algorithm Transformations ( Dat ) Forlow - Power Adaptive Signal

LOW-POWER ADAPTIVE SIGNAL PROCESSINGy Manish Goel and Naresh R. Shanbhag Coordinated Science Lab./ECE Department, Univ. of Illinois at Urbana-Champaign, 1308 W. Main Street, Urbana IL-61801. E-mail : [mgoel,shanbhag]@uivlsi.csl.uiuc.edu ABSTRACT Presented in this paper are algorithm transformation techniques for adaptive signal processing, which allow dynamic alteration of algorithm properties in response to signal nonstationarities. 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 (PD), 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 con guration incorporating signal transition activity is presented. Next, practical SMA schemes are developed, which achieved power reduction by a combination of powering down the lter taps and modifying the coe cients. The DAT-based adaptive lter 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.