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Effects of Analog-VLSI Hardware on the Performance of the LMS Algorithm

  • Gonzalo Carvajal
  • Miguel Figueroa
  • Seth Bridges
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

Abstract

Device mismatch, charge leakage and nonlinear transfer functions limit the resolution of analog-VLSI arithmetic circuits and degrade the performance of neural networks and adaptive filters built with this technology. We present an analysis of the impact of these issues on the convergence time and residual error of a linear perceptron using the Least-Mean-Square (LMS) algorithm. We also identify design tradeoffs and derive guidelines to optimize system performance while minimizing circuit die area and power dissipation.

Keywords

Mean Square Error Learning Rate Minimal Mean Square Error Convergence Time Forward Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gonzalo Carvajal
    • 1
  • Miguel Figueroa
    • 1
  • Seth Bridges
    • 2
  1. 1.Department of Electrical EngineeringUniversidad de ConcepciónChile
  2. 2.Computer Science and EngineeringUniversity of WashingtonUSA

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