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Enabling Cross-Layer Reliability and Functional Safety Assessment Through ML-Based Compact Models
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.
Modeling Gate-Level Abstraction Hierarchy Using Graph Convolutional Neural Networks to Predict Functional De-Rating Factors
In the preliminary phase of the work, the important goal is making a GCN which able to take a gate-level netlist as input information after transforming it into the Probabilistic Bayesian Graph in the form of Graph Modeling Language (GML).
Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features
Selective mitigation or selective hardening is an effective technique to obtain a good trade-off between the improvements in the overall reliability of a circuit and the hardware overhead induced by the hardening techniques.
On the Estimation of Complex Circuits Functional Failure Rate by Machine Learning Techniques
De-Rating or Vulnerability Factors are a major feature of failure analysis efforts mandated by today's Functional Safety requirements.
Machine Learning to Tackle the Challenges of Transient and Soft Errors in Complex Circuits
One part of this reference dataset is used to train the machine learning model and the remaining is used to validate and benchmark the accuracy of the trained tool.
Signal Processing
