An important consideration for neural hardware is its sensitivity to input and weight errors. In this paper, an empirical study is performed to analyze the sensitivity of feedforward neural networks for Gaussian noise to input and weight. 30 numbers of FFANN is taken for four different classification tasks. Least sensitive network for input and weight error is chosen for further study of fault tolerant behavior of FFANN. Weight stuck-at zero fault is selected to study error metrics of fault tolerance. Empirical results for a WSZ fault is demonstrated in this paper.

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