Defect tolerant implementations of feed-forward and recurrent neural networks

Abstract

Many of the defect tolerant techniques employed to achieve wafer-scale integration can also be used to construct flexible and scalable architectures. These techniques are applied to two artificial neural networks: a feed-forward analog network with backpropagation and an efficient digital recurrent network.<>