  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.
