GNN-RE: Graph Neural Networks for Reverse Engineering of Gate-Level Netlists
| Autor: | Satwik Patnaik, Abhrajit Sengupta, Hani Saleh, Mahmoud Al-Qutayri, Lilas Alrahis, Ozgur Sinanoglu, Johann Knechtel, Baker Mohammad |
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| Rok vydání: | 2022 |
| Předmět: |
Reverse engineering
Hardware security module Theoretical computer science Source data Computer science Feature vector Feature extraction computer.software_genre Computer Graphics and Computer-Aided Design Logic gate Netlist Node (circuits) Electrical and Electronic Engineering computer Software Hardware_LOGICDESIGN |
| Zdroj: | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41:2435-2448 |
| ISSN: | 1937-4151 0278-0070 |
| Popis: | This work introduces a generic, machine learning (ML)-based platform for functional reverse engineering (RE) of circuits. Our proposed platform GNN-RE leverages the notion of graph neural networks (GNNs) to (i) represent and analyze flattened/ unstructured gate-level netlists, (ii) automatically identify the boundaries between the modules or sub-circuits implemented in such netlists and (iii) classify the sub-circuits based on their functionalities. For GNNs in general, each graph node is tailored to learn about its own features and its neighboring nodes, which is a powerful approach for the detection of any kind of sub-graphs of interest. For GNN-RE, in particular, each node represents a gate and is initialized with a feature vector that reflects on the functional and structural properties of its neighboring gates. GNN-RE also learns the global structure of the circuit, which facilitates identifying the boundaries between subcircuits in a flattened netlist. Initially, to provide high-quality data for training of GNN-RE, we deploy a comprehensive dataset of foundational designs/components with differing functionalities, implementation styles, bit-widths, and interconnections. GNN-RE is then tested on the unseen shares of this custom dataset, as well as the EPFL benchmarks, the ISCAS-85 benchmarks, and the 74X series benchmarks. GNN-RE achieves an average accuracy of 98:82% in terms of mapping individual gates to modules, all without any manual intervention or post-processing. We also release our code and source data 1. |
| Databáze: | OpenAIRE |
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