52 citations · 600 across the 60 of their papers we have counts for
5 papers · 1 filter
Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks
Rupesh Raj Karn, Johann Knechtel, Ozgur Sinanoglu
As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require careful attention. Here, we presen…
SALAD: Systematic Assessment of Machine Unlearning on LLM-Aided Hardware Design
Zeng Wang, Minghao Shao, Rupesh Karn +6
Large Language Models (LLMs) offer transformative capabilities for hardware design automation, particularly in Verilog code generation. However, they also pose significant data sec…
LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust
Zeng Wang, Lilas Alrahis, Likhitha Mankali +2
Chip design is about to be revolutionized by the integration of large language, multimodal, and circuit models (collectively LxMs). While exploring this exciting frontier with trem…
Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs
Lilas Alrahis, Johann Knechtel, Ozgur Sinanoglu
Graph neural networks (GNNs) have pushed the state-of-the-art (SOTA) for performance in learning and predicting on large-scale data present in social networks, biology, etc. Since…
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…