Energy efficient, secure and noise robust deep learning for the internet of things

Abstract

The objective of this research is to design an energy efficient, secure and noise robust deep learning system for the Internet of Things (IoTs). The research particularly focuses on energy efficient training of deep learning, adversarial machine learning, and noise robust deep learning. To enable energy efficient training of deep learning, the research studies impact of a limited precision training of various types of neural networks like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For CNNs, the work proposes dynamic precision scaling algorithm, and precision flexible computing unit to accelerate CNNs training. For RNNs, the work studies impact of various hyper-parameters to enable low precision training of RNNs and proposes low precision computing unit with stochastic rounding. To enhance the security of deep learning, the research proposes cascade adversarial machine learning and additional regularization using a unified embedding for image classification and low level (pixel level) similarity learning. Noise robust and resolution-invariant image classification is also achieved by adding this low level similarity learning. Mixture of pre-processing experts model is proposed for noise robust object detection network without sacrificing accuracy for the clean images.