ABSTRACT
Hardware annealing, which is a paralleled version of mean-field annealing in analog networks, is an efficient method of finding the optimal solutions for cellular neural networks. It does not require any stochastic procedure and henceforth can be very fast. Once the energy of the network is increased, the hardware annealing searches for the globally minimum energy state by gradually increasing the gain of neurons. In typical non-optimization problems, it also provides enough energy to frozen neurons caused by ill-conditioned initial states.
