Fault Tolerance of Artificial Neural Networks With Applications in Critical Systems

One of the key benefits or future hardware implementations of certain artificial neural networks (ANN's) is their apparently "built-in" fault tolerance which makes them potential candidates for critical tasks with high reliability requirements. This paper investigates the fault-tolerance characteristics of time-continuous, recurrent ANN's that can be used to solve optimization problems. The principle of operation and the performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANN's are then subjected to up to 13 simultaneous "stuckat-1" or "stuck-at-O" faults for network sizes of up to 900 "neurons." The effect of these faults on the performance is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a realtime distributed processing system by generating new task allocations during the reconfiguration of the syitem. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault-tolerant network are discussed.