Special Session: Machine Learning for Embedded System Design

Embedded systems are becoming increasingly complex, which has led to a productivity crisis in their design and verification. Although conventional design automation coupled with IP and platform reuse techniques have led to leaps in design productivity improvement, they face fundamental limits given that most design optimization and verification problems remain NP-hard and that reuse of pre-designed IP blocks and platforms inherently limits flexibility and optimality. At the same time, machine learning (ML) has recently made unprecedented advances and created phenomenal impact in various computing applications. In particular, application of ML techniques as a way to extract knowledge and learn from existing design, optimization and verification data has recently seen a lot of excitement and promise at lower physical and integrated circuit levels of abstraction. Using ML has the potential to similarly close the complexity gap in embedded system design, but corresponding ML-based approaches for embedded system optimization and verification at higher levels of abstraction are still at their infancy. This paper presents the current state of the art, along with opportunities and open challenges, in the application of ML methods for embedded system design and optimization. We discuss design and optimization at different levels of abstraction ranging from system-level modeling and optimization and high-level synthesis to RTL and micro-architecture design, bringing together perspectives from different communities in both academia and industry.