6 papers
Overcoming Data Scarcity and Confidentiality in Hardware Assurance via Synthetic Generation
Gijung Lee, Ronald Wilson, Damon L. Woodard +1
Hardware assurance relies on scanning electron microscopy (SEM) to verify nanoscale structures, but assembling the large, high-quality datasets required for automated analysis is i…
Resource Utilization of Differentiable Logic Gate Networks Deployed on FPGAs
Stephen Wormald, Gilon Kravatsky, Damon Woodard +1
On-edge machine learning (ML) often strives to maximize the intelligence of small models while miniaturizing the circuit size and power needed to perform inference. Meeting these n…
Potentials and Pitfalls of Applying Federated Learning in Hardware Assurance
Gijung Lee, Wavid Bowman, Olivia Dizon-Paradis +4
As microelectronics flourish and outsourcing of the design and manufacturing stages of integrated circuits (ICs) and printed circuit boards (PCBs) becomes the norm, microelectronic…
DECIFR: Domain-Aware Exfiltration of Circuit Information from Federated Gradient Reconstruction
Gijung Lee, Wavid Bowman, Olivia P. Dizon-Paradis +4
Federated Learning (FL) is a promising approach for multiparty collaboration as a privacy-preserving technique in hardware assurance, but its security against adversaries with doma…
A Data-Free Membership Inference Attack on Federated Learning in Hardware Assurance
Gijung Lee, Wavid Bowman, Olivia P. Dizon-Paradis +4
Federated Learning (FL) is an emerging solution to the data scarcity problem for training deep learning models in hardware assurance. While FL is designed to enhance privacy by not…
eXpLogic: Explaining Logic Types and Patterns in DiffLogic Networks
Stephen Wormald, David Koblah, Matheus Kunzler Maldaner +2
Constraining deep neural networks (DNNs) to learn individual logic types per node, as performed using the DiffLogic network architecture, opens the door to model-specific explanati…