AppGNN: Approximation-Aware Functional Reverse Engineering using Graph Neural Networks

23 August 2022

Papers citing "AppGNN: Approximation-Aware Functional Reverse Engineering using Graph Neural Networks"

8 / 8 papers shown

Title
Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate Yifei Jin Dimitrios Koutlis Hector Bandala Marios Daoutis 44 0 0 07 Feb 2025
A Benchmark on Directed Graph Representation Learning in Hardware Designs Haoyu Wang Yinan Huang Nan Wu Pan Li OOD 38 1 0 09 Oct 2024
MaliGNNoma: GNN-Based Malicious Circuit Classifier for Secure Cloud FPGAs Lilas Alrahis Hassan Nassar Jonas Krautter Dennis R. E. Gnad Lars Bauer Jörg Henkel M. Tahoori 21 2 0 04 Mar 2024
Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designs Yingjie Li Mingju Liu Alan Mishchenko Cunxi Yu 16 6 0 09 Nov 2023
Graph Neural Networks for Hardware Vulnerability Analysis -- Can you Trust your GNN? Lilas Alrahis Ozgur Sinanoglu 14 2 0 29 Mar 2023
PoisonedGNN: Backdoor Attack on Graph Neural Networks-based Hardware Security Systems Lilas Alrahis Satwik Patnaik Muhammad Abdullah Hanif Muhammad Shafique Ozgur Sinanoglu 14 13 0 24 Mar 2023
TrojanSAINT: Gate-Level Netlist Sampling-Based Inductive Learning for Hardware Trojan Detection Hazem Lashen Lilas Alrahis J. Knechtel Ozgur Sinanoglu 17 10 0 27 Jan 2023
Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs Lilas Alrahis J. Knechtel Ozgur Sinanoglu GNN AI4CE 33 20 0 29 Nov 2022