Impact of white noise in artificial neural networks trained for classification: Performance and noise mitigation strategies
N. Semenova,
D. Brunner
Abstract:In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and complex physical networks provide significant advantages in speed and energy efficiency, but are potentially susceptible to internal noise when compared to digital emulations of such networks. In this work, we consider how additive and multiplicative Gaussian white noise on the neuronal level can affect the accuracy of the network when app… Show more
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