Supervisor: Muhammad Shafique
Cross-Layer Techniques for Energy-Efficiency and Resiliency of
Advanced Machine Learning Architectures
Machine Learning (ML) algorithms have shown high level of accuracy in several tasks, therefore ML-based applications are widely used in many systems and platforms. However, the development of efficient ML-based systems requires addressing two key research problems: energy-efficiency and resilience. Current trends show the growing interest in the community for complex ML models, such as Deep Neural Networks (DNNs), Capsule Networks (CapsNets), and Spiking Neural Networks (SNNs). Besides their high learning capabilities, their complexity pose several challenges to address the above-discussed research problems. This work investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build resilient and energy-efficient architectures for these advanced ML networks.