Energy Efficiency and Robustness of Advanced Machine Learning Architectures: A Cross-Layer Approach [Hardcover]

Chapter 01 Introduction Chapter 02 Background and Related Work Chapter 03 Hardware and Software Optimizations for Capsule Networks Chapter 04 Adversarial Security Threats for DNNs and CapsNets Chapter 05 Integration of Multiple Design Objectives into NAS Frameworks for CapsNets and DNNs Chapter 06 Efficient Optimizations for Spiking Neural Networks on Neuromorphic Hardware Chapter 07 Security Threats for SNNs on Discrete and Event-Based Data? Chapter 08 Conclusion and Outlook Bibliography

This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks.

Alberto Marchisio received his B.Sc. and M.Sc. degrees in Electronic Engineering from Politecnico di Torino, Turin, Italy, in October 2015 and April 2018, respectively. He received his Ph.D. degree in Computer Science from the Technische Universit?t Wien (TU Wien) Informatics Doctoral College Resilient Embedded Systems, Vienna, Austria, in September 2023. Currently, he is a Research Group Leader with the eBrain Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), United Arab Emirates. His main research interests include hardware and software optimizations for machine learning, brain-inspired computing, VLSI architecture design, emerging computing technologies, robust design, and approximate computing for energy efficiency. He (co-)authored 30+ papers in prestigious international conferences and journals. He received the honorable mention at the Italian National Finals of Maths Olympic Games in 2012, and the Richard Newton Young Fellow Award in 2019.

Muhammad Shafique (M11 - SM16) received his Ph.D. degree in Computer Science from the Karlsruhe Institute of Technology (KIT), Germany, in 2011. l“2