Nonlinear switched capacitor `neural´ networks for optimization problems

A systematic approach is presented for the design of analog neural nonlinear programming solvers using switched-capacitor (SC) integrated circuit techniques. The method is based on formulating a dynamic gradient system whose state evolves in time toward the solution point of the corresponding programming problem. A neuron cell for the linear and the quadratic problem suitable for monolithic implementation is introduced. The design of this neuron and its corresponding synapses using SC techniques is considered in detail. An SC circuit architecture based on a reduced set of basic building blocks with high modularity is presented. Simulation results using a mixed-mode simulator (DIANA) and experimental results from breadboard prototypes are included, illustrating the validity of the proposed techniques