Synaptic 1/f noise injection for overfitting suppression in hardware neural networks

Abstract

Overfitting is a common and critical challenge for neural networks trained with limited dataset. The conventional solution is software-based regularization algorithms such as Gaussian noise injection. Semiconductor noise, such as 1/f noise, in artificial neuron/synapse devices, which is often regarded as undesirable disturbance to the hardware neural networks (HNNs), could also play a useful role in suppressing overfitting, but that is as yet unexplored. In this work, we proposed the idea of using 1/f noise injection to suppress overfitting in different neural networks, and demonstrated that: (i) 1/f noise could suppress the overfitting in multilayer perceptron (MLP) and long short-term memory (LSTM); (ii) 1/f noise and Gaussian noise performs similarly for the MLP but differently for the LSTM; (iii) the superior performance of 1/f noise on LSTM can be attributed to its intrinsic long range dependence. This work reveals that 1/f noise, which is common in semiconductor devices, can be a useful solution to suppress the overfitting in HNNs, and more importantly, further evidents that the imperfectness of semiconductor devices is a rich mine of solutions to boost the development of brain-inspired hardware technologies in the artificial intelligence era.