5 papers
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…
Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis
Mohyeu Hussain, David Koblah, Reiner Dizon-Paradis +1
Analog-mixed-signal (AMS) circuits are highly non-linear and operate on continuous real-world signals, making them far more difficult to model with data-driven AI than digital bloc…
Scalable IP Mimicry: End-to-End Deceptive IP Blending to Overcome Rectification and Scale Limitations of IP Camouflage
Junling Fan, George Rushevich, Giorgio Rusconi +3
Semiconductor intellectual property (IP) theft incurs estimated annual losses ranging from 600 billion. Despite initiatives like the CHIPS Act, many semiconductor…