Performance and Fault-Tolerance of Neural Networks for Optimization

摘要

One of the key benefits of future hardware implementations of certain ArtificialNeural Networks (ANNs) 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 ANNs that can be used to solve optimization problems. The performance of these networks is first illustrated by using well-known model problems like the Traveling Salesman Problem and the Assignment Problem. The ANNs are then subjected to up to 13 simultaneous stuck-at-1 or stuck-at-0 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 real time distributed processing system by generating new task allocations during the reconfiguration of the system. 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.