An evident challenge ahead for the integrated circuit (IC) industry is the investigation and
development of methods to reduce the design complexity ensuing from growing process …

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 …

Microservice architecture is a design philosophy that achieves decoupling by decomposing
a monolithic application into multiple lightweight microservices. Meanwhile, edge computing …

Graph neural networks (GNNs) have shown great success in detecting intellectual property
(IP) piracy and hardware Trojans (HTs). However, the machine learning community has …

With transistors scaling down, aging effects become increasingly significant in circuit design.
Thus, the aging-aware cell timing model is necessary for evaluating the aging-induced delay …

In recent years, Graph Neural Networks (GNNs) have made significant advances in
processing structured data. However, most of them primarily adopted a model-centric …

Accurate and efficient identification of reliability-critical paths (RCPs) not only facilitates fault
localization and troubleshooting but also allows circuit designers to improve circuit reliability …

We propose TrojanSAINT, a graph neural network (GNN)-based hardware Trojan (HT)
detection scheme working at the gate level. Unlike prior GNN-based art, TrojanSAINT …

With the downscaling of CMOS technology, device aging induced by hot carrier injection
and bias temperature instability effects poses severe challenges to timing analysis of digital …

Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning
(QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility …