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A One-Dimensional Analog VLSI Implementation for Nonlinear Real-Time Signal Preprocessing

Reconstruction of given noisy data is an ill-posed problem and a computationally intensive task. Nonlinear regularization techniques are used to find a unique solution under certain constraints. In our contribution we present a parallel mixed-signal architecture which solves this nonlinear problem with in microseconds. By connecting all parallel cells in a circular manner it is possible to process noisy data vectors of infinite length. This is achieved by virtually shifting the nonlinear adaptive filter kernel over the noisy data vector. Additionally, we focus on the interaction between theory, discretization, numerical simulations, macro-modeling, and analog VLSI implementation for a theoretically well understood class of computer vision in an exemplary and paradigmatic way. A one-dimensional (1D) experimental chip has been fabricated using 0.8 ?m CMOS technology. On-chip measurements are shown to agree with results from numerical simulations. Results from applying the 1D chip to nonlinear smoothing of two-dimensional image data will also be given correspondence.