Enabling Cross-Layer Reliability and Functional Safety Assessment Through ML-Based Compact Models

TL;DR

This paper introduces a machine learning approach to create compact, reliable, and confidential models for hierarchical system design, enabling better assessment of reliability and safety across different system levels.

Contribution

It proposes a novel ML-based method to generate integrated compact models that preserve confidentiality and accuracy for hierarchical reliability and safety assessment.

Findings

Provides consistent and accurate compact models

Ensures confidentiality and IP protection

Facilitates hierarchical reliability and safety analysis

Abstract

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.