arXiv:2606. 25589v1 Announce Type: new Abstract: As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require careful attention.
By Rupesh Raj Karn, Johann Knechtel, Ozgur Sinanoglu
arXiv:2608.29054v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly i...
By Shuomin Xue, Jingyuan Li, Ju Jia, Jingxuan Yu, Xiaojun Jia
arXiv:2608.28188v1 Announce Type: new
Abstract: Circuit Representation Learning (CRL) offers a powerful paradigm to guide and optimize core Electronic Design Automation (EDA) tasks, but its practical...
By Jingyi Zhou, Zhengyuan Shi, Jiaying Zhu, Ziyang Zheng, Qiang Xu
arXiv:2601. 18696v5 Announce Type: replace Abstract: Hardware trojans are malicious circuits which compromise the functionality and security of an integrated circuit (IC).
By Paul Whitten, Francis Wolff, Chris Papachristou
arXiv:2607. 21633v1 Announce Type: new Abstract: Logic Gate Networks (LGNs) implement computation through compositions of Boolean operations, yet unlike classical Boolean circuits, existing LGNs do not reliably benefit from increased depth.
By Taegun An, Dohun kim, Haebeom Lee, Changhee Joo
arXiv:2607. 07089v1 Announce Type: new Abstract: Relational Deep Learning (RDL) has become a standard methodology for machine learning on relational databases: the database is encoded as a heterogeneous temporal graph in which tuples become nodes and primary-key to foreign-key (PK-FK) dependencies become typed edges, over which a graph neural network is trained for downstream prediction.
By Alan Gany, Bogdan Cautis, Silviu Maniu