计算机工程2025，Vol.51Issue(6)：29-37,9.DOI:10.19678/j.issn.1000-3428.0068882
基于图注意力网络的门级网表功能识别
Gate-level Netlist Function Recognition Based on Graph Attention Networks
摘要
Abstract
With the rapid increase in the complexity of integrated circuit design,a trend of globalization and division of labor has emerged,necessitating the involvement of an increasing number of third-party Intellectual Property(IP)core providers.The widespread use of third-party IP cores introduces risks of hardware trojans.To detect and evaluate the presence of hardware trojans and their potential functionalities in third-party IP cores,there is an urgent need to explore feasible hardware security evaluation methods for IP cores.The functional identification of digital circuit modules has garnered significant attention as a fundamental research area in hardware trojan analysis.In this study,the task of circuit function detection is transformed into a multiclassification problem.By leveraging the characteristics of the circuit and graph data structures,a gate-level circuit function classification and detection method based on Graph Attention Networks(GAT)is proposed.First,to address the lack of functional identification datasets for gate-level netlists,a representative set of Register Transfer Level(RTL)codes is collected and synthesized to generate gate-level netlists,thereby constructing a gate-level circuit dataset of appropriate scale and diversity.Subsequently,to extract and process the circuit feature information,a software tool based on text recognition is developed.This tool maps the complex interconnections of circuits into a structured and concise JSON(JavaScript Object Notation)format,thereby facilitating neural network processing.Finally,a graph attention neural network is employed to train a multiclassifier using the constructed gate-level netlist dataset.After training,the multiclassifier becomes capable of classifying and identifying unknown gate-level circuits.The experimental results demonstrate that the classifier,after learning from more than 3 000 netlists in the self-built dataset,achieves a classification accuracy of 90％for 645 netlists across six categories.关键词
集成电路/电路网表/功能识别/深度学习/图神经网络Key words
integrated circuit/circuit netlist/function recognition/deep learning/graph neural network分类
计算机与自动化引用本文复制引用
秦永旺,张洋,胡星,刘胜,李少青..基于图注意力网络的门级网表功能识别[J].计算机工程,2025,51(6):29-37,9.基金项目
国家自然科学基金重点项目(61832018). （61832018）
