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

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.