Image recognition with an analog neural net chip

The authors applied an analog neural net chip to several machine vision tasks, among them: locate the address blocks on mail pieces, find handwritten text on checks, and discriminate between handwritten and machine printed characters. The chip, operating as a co-processor of a workstation, provides a speed-up of about a factor of 1000, compared with the workstation. The computation speed achieved lies between one and ten billion multiply-accumulates per second. The neural net chip is based on building blocks, `neurons´, that can be arranged in various network architectures. The data flow is optimized for implementing large, structured neural nets, and is also suited for any task where signals are to be convolved with many kernels. Some of the networks are trained on the neural net chip with a weight-perturbation learning algorithm that was adapted to work with the coarse quantization of the weights and the states in the chip