Abstract
AbstractThe drive for automation and constant monitoring has led to rapid development in the field of Machine Learning (ML). The high accuracy offered by the state-of-the-art ML algorithms like Deep Neural Networks (DNNs) has paved the way for these algorithms to being used even in the emerging safety-critical applications, e.g., autonomous driving and smart healthcare. However, these applications require assurance about the functionality of the underlying systems/algorithms. Therefore, the robustness of these ML algorithms to different reliability and security threats has to be thoroughly studied and mechanisms/methodologies have to be designed which result in increased inherent resilience of these ML algorithms. Since traditional reliability measures like spatial and temporal redundancy are costly, they may not be feasible for DNN-based ML systems which are already super computer and memory intensive. Hence, new robustness methods for ML systems are required. Towards this, in this chapter, we present our analyses illustrating the impact of different reliability and security vulnerabilities on the accuracy of DNNs. We also discuss techniques that can be employed to design ML algorithms such that they are inherently resilient to reliability and security threats. Towards the end, the chapter provides open research challenges and further research opportunities.
Highlights
Machine learning (ML) has emerged as the principal tool for performing complex tasks which are impractical to code by humans
Aging is the gradual degradation of the hardware due to different physical phenomena like Hot carrier Injection (HCI), Negative-Bias Temperature Instability (NBTI), and Electromigration (EM)
Neurons are the fundamental computational units in a neural network where each neuron performs a weighted sum of inputs, using the inputs and the weights associated with each input connection of the neuron
Summary
Machine learning (ML) has emerged as the principal tool for performing complex tasks which are impractical (if not impossible) to code by humans. ML techniques provide machines the capability to learn from experience and thereby learn to perform complex tasks without much (if any) human intervention. Deep Learning (DL), using Deep Neural Networks (DNNs), has shown state-of-the-art accuracy, even surpassing human-level accuracy in some cases, for many applications [31]. These applications include, but are not limited to, object detection and localization, speech recognition, language translation, and video processing [31]. The state-of-the-art performance of the DL-based methods has led to the use of DNNs in complex safety-critical applications, for example, autonomous driving [11] and smart healthcare [10].
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