39 citations · 130 across the 7 of their papers we have counts for
5 papers · 1 filter
GNN4REL: Graph Neural Networks for Predicting Circuit Reliability Degradation
Lilas Alrahis, Johann Knechtel, Florian Klemme +2
Process variations and device aging impose profound challenges for circuit designers. Without a precise understanding of the impact of variations on the delay of circuit paths, gua…
Modeling and Predicting Transistor Aging under Workload Dependency using Machine Learning
Paul R. Genssler, Hamza E. Barkam, Karthik Pandaram +2
The pivotal issue of reliability is one of colossal concern for circuit designers. The driving force is transistor aging, dependent on operating voltage and workload. At the design…
Brain-Inspired Hyperdimensional Computing: How Thermal-Friendly for Edge Computing?
Paul R. Genssler, Austin Vas, Hussam Amrouch
Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning (ML) methods. It is based on large vectors of binary or bipolar symbols and a few simple mathematica…
Automated Design Approximation to Overcome Circuit Aging
Konstantinos Balaskas, Georgios Zervakis, Hussam Amrouch +2
Transistor aging phenomena manifest themselves as degradations in the main electrical characteristics of transistors. Over time, they result in a significant increase of cell propa…
HW/SW Co-design for Reliable TCAM-based In-memory Brain-inspired Hyperdimensional Computing
Simon Thomann, Paul R. Genssler, Hussam Amrouch
Brain-inspired hyperdimensional computing (HDC) is continuously gaining remarkable attention. It is a promising alternative to traditional machine-learning approaches due to its ab…