17 citations · 89 across the 31 of their papers we have counts for
4 papers · 1 filter
Composing Graph Theory and Deep Neural Networks to Evaluate SEU Type Soft Error Effects
Aneesh Balakrishnan, Thomas Lange, Maximilien Glorieux +2
Rapidly shrinking technology node and voltage scaling increase the susceptibility of Soft Errors in digital circuits. Soft Errors are radiation-induced effects while the radiation…
The Validation of Graph Model-Based, Gate Level Low-Dimensional Feature Data for Machine Learning Applications
Aneesh Balakrishnan, Thomas Lange, Maximilien Glorieux +2
As an alternative to traditional fault injection-based methodologies and to explore the applicability of modern machine learning algorithms in the field of reliability engineering,…
Modeling Gate-Level Abstraction Hierarchy Using Graph Convolutional Neural Networks to Predict Functional De-Rating Factors
Aneesh Balakrishnan, Thomas Lange, Maximilien Glorieux +2
The paper is proposing a methodology for modeling a gate-level netlist using a Graph Convolutional Network (GCN). The model predicts the overall functional de-rating factors of seq…
Representing Gate-Level SET Faults by Multiple SEU Faults at RTL
Ahmet Cagri Bagbaba, Maksim Jenihhin, Raimund Ubar +1
The advanced complex electronic systems increasingly demand safer and more secure hardware parts. Correspondingly, fault injection became a major verification milestone for both sa…