A nonlinear switched-capacitor network for edge detection in early vision

A nonlinear switched-capacitor (SC) network for solving the early vision variational problem of edge detection has been designed and constructed using standard SC techniques and a novel nonlinear externally controlled SC resistive element. This new SC element allows, to a limited extent, the form of the variational problem to be "programmable". This allows nonconvex variational problems to be solved by the network using continuation-type methods. Appropriately designed SC networks are guaranteed to converge to a locally stable steady-state. As well, SC networks offer increased accuracy over analog networks composed of nonlinear resistances built from multiple MOSFETs. The operation of the network was analyzed and found to be equivalent to the numerical analysis minimization algorithm of gradient descent. The network's capabilities were demonstrated by "programming" the network to perform the graduated nonconvexity algorithm. A high-level functional network simulation was used to verify the correct operation of the GNC algorithm. A one-dimensional six node CMOS VLSI test chip was designed, simulated and submitted for fabrication.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat