IMAGE RECOGNITION WITH AN ANALOG NEURAL-NET CHIP

We developed a machine vision system around an analog neural net chip and used it in several applications. Some of them were: locating the address blocks on mail pieces, finding the identification numbers on rail cars, and discriminating between handwritten and machine-printed characters. The chip, operating as a coprocessor of a workstation, provides a speed-up of a factor of 1000, compared with the workstation. The computation speed achieved lies between one and ten billion multiply-accumulates/s. The neural net chip is based on building blocks, neurons, that can be arranged in various network architectures. The dataflow is optimized for implementing large, structured neural nets, and is also suited for any task in which 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.