Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems
It tackles the problem of insufficient computing infrastructure for ML applications, focusing on efficiency, reliability, and security, but appears incremental as it summarizes challenges and outlines a methodology without presenting new experimental results.
The paper addresses the challenges in developing efficient, reliable, and secure machine learning systems for real-world applications, proposing an agile design methodology to generate such systems based on user-defined constraints and objectives.
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.