Enabling Cross-Layer Reliability and Functional Safety Assessment Through ML-Based Compact Models
This addresses the challenge for electronic design providers and users in handling IP confidentiality and information propagation in complex systems, though it appears incremental in applying ML to an existing modeling framework.
The paper tackles the problem of managing reliability and functional safety information across hierarchical design flows by proposing a machine-learning-based approach to integrate individual models into a single compact model, enabling consistency, accuracy, and confidentiality for providers and users.
Typical design flows are hierarchical and rely on assembling many individual technology elements from standard cells to complete boards. Providers use compact models to provide simplified views of their products to their users. Designers group simpler elements in more complex structures and have to manage the corresponding propagation of reliability and functional safety information through the hierarchy of the system, accompanied by the obvious problems of IP confidentiality, possibility of reverse engineering and so on. This paper proposes a machine-learning-based approach to integrate the many individual models of a subsystem's elements in a single compact model that can be re-used and assembled further up in the hierarchy. The compact models provide consistency, accuracy and confidentiality, allowing technology, IP, component, sub-system or system providers to accompany their offering with high-quality reliability and functional safety compact models that can be safely and accurately consumed by their users.