Faiq Khalid

TL;DR: An overview of the current and emerging trends in designing highly efficient, reliable, secure and scalable machine learning architectures for IoT devices and presents a roadmap that can help in addressing the highlighted challenges and thereby designing scalable, high-performance, and energy efficient architectures for performing machine learning on the edge.

TL;DR: This paper summarizes the state of the art of existing work on anomaly detection, fault-tolerance, and self-healing, and adds a number of other methods applicable to achieve resilience in an IoT, particularly on non-intrusive methods ensuring data integrity in the network.

TL;DR: The current trends of such optimizations for deep learning have to be performed at both software and hardware levels are surveyed and key open research mid-term and long-term challenges are discussed.

TL;DR: An overview of the challenges being faced in ensuring reliable and secure execution of DNNs is provided and several techniques for analyzing and mitigating the reliability and security threats in machine learning systems are presented.

TL;DR: The current progress, challenges, research opportunities, and research opportunities in the domain of robust systems for machine learning-based applications for reliability and security are highlighted.