Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems
Hardware Architecture2022-06-22v1Cryptography and SecurityComputer Vision and Pattern RecognitionDistributed, Parallel, and Cluster ComputingMachine Learning
The real-world use cases of Machine Learning (ML) have exploded over the past few years. However, the current computing infrastructure is insufficient to support all real-world applications and scenarios. Apart from high efficiency requirements, modern ML systems are expected to be highly reliable against hardware failures as well as secure against adversarial and IP stealing attacks. Privacy concerns are also becoming a first-order issue. This article summarizes the main challenges in agile development of efficient, reliable and secure ML systems, and then presents an outline of an agile design methodology to generate efficient, reliable and secure ML systems based on user-defined constraints and objectives.
@article{arxiv.2204.09514,
title = {Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems},
author = {Shail Dave and Alberto Marchisio and Muhammad Abdullah Hanif and Amira Guesmi and Aviral Shrivastava and Ihsen Alouani and Muhammad Shafique},
journal= {arXiv preprint arXiv:2204.09514},
year = {2022}
}
Comments
Appears at 40th IEEE VLSI Test Symposium (VTS 2022), 14 pages