title: Weight shifting techniques for self-recovery neural networks.
abstract: In this paper, a self-recovery technique of feedforward neural networks called weight shifting and its analytical models are proposed. The technique is applied to recover a network when some faulty links and/or neurons occur during the operation. If some input links of a specific neuron are detected faulty, their weights will be shifted to healthy links of the same neuron. On the other hand, if a faulty neuron is encountered, then we can treat it as a special case of faulty links by considering all the output links of that neuron to be faulty. The aim of this technique is to recover the network in a short time without any retraining and hardware repair. We also propose the hardware architecture for implementing this technique.
authors.affiliation: Center for Adv. Comput. Studies, Southwestern Louisiana Univ., Lafayette, LA.
authors.forename: C
authors.initials: C
authors.lastname: Khunasaraphan
authors.forename: K
authors.initials: K
authors.lastname: Vanapipat
authors.forename: C
authors.initials: C
authors.lastname: Lursinsap