Noise Tolerance of an Energy-Scalable Deep Learning Model with Two Extreme Bit-Precisions

Abstract

In this paper, we perform the noise analysis on an energy-scalable deep learning model with two extreme bit-precisions, named MixNet. In real-world applications, there might be a great deal of noisy inputs that are collected from mobile sensors, and the training is performed on those noisy datasets. According to our initial set of experiments, MixNet has lower sensitivity to the noise in the training dataset, when compared to the original CNN model with high-precision. As a result, it is expected that the MixNet can be trained better even in a noisy environment than the original high-precision deep learning models.