GNN-RE: Graph Neural Networks for Reverse Engineering of Gate-Level Netlists

Lilas Alrahis, Abhrajit Sengupta, Johann Knechtel, Satwik Patnaik, Hani Saleh, Baker Mohammad, Mahmoud Al-Qutayri, Ozgur Sinanoglu

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Keywords

  • Benchmark testing
  • DH-HEMTs
  • Feature extraction
  • Gate-level netlist
  • Graph neural networks
  • Hardware security
  • Integrated circuit modeling
  • Libraries
  • Logic gates
  • Machine learning.
  • Reverse engineering
  • Reverse engineering

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'GNN-RE: Graph Neural Networks for Reverse Engineering of Gate-Level Netlists'. Together they form a unique fingerprint.

Cite this