Impact of internal noise on deep neural networks

Nadezhda Semenova; Javier Porte; Maxime Jacquot; Laurent Larger; Daniel Brunner

doi:10.1117/12.2594278

1 August 2021 Impact of internal noise on deep neural networks

Nadezhda Semenova, Javier Porte, Maxime Jacquot, Laurent Larger, Daniel Brunner

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041L (2021) https://doi.org/10.1117/12.2594278
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

We analyze the fundamental impact of noise propagation in deep neural network (DNN) comprising nonlinear neurons and with connections optimized by training. Our motivation is to understand the impact of noise in analogue neural network realizations. We consider the influence of additive and multiplicative, correlated and uncorrelated types of internal noise in DNNs. We find general properties of the noise impact depending on the noise type, activation function, depth and the statistics of connection matrices and show that noise accumulation can be efficiently avoided. Our work is based on analytical methods predicting the noise levels in all layers of the network.

Conference Presentation

Citation Download Citation

Nadezhda Semenova, Javier Porte, Maxime Jacquot, Laurent Larger, and Daniel Brunner "Impact of internal noise on deep neural networks", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041L (1 August 2021); https://doi.org/10.1117/12.2594278

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