Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Accelerating Hardware Verification with Graph Models

Raghul Saravanan, Sreenitha Kasarapu, Sai Manoj Pudukotai Dinakarrao

TL;DR

GraphFuzz, a graph-based hardware fuzzer designed for gate-level netlist verification, demonstrates an average prediction accuracy of 80% and bug detection accuracy of 70%, highlighting the potential of graph-based methods for enhancing hardware verification.

Abstract

The increasing complexity of modern processor and IP designs presents significant challenges in identifying and mitigating hardware flaws early in the IC design cycle. Traditional hardware fuzzing techniques, inspired by software testing, have shown promise but face scalability issues, especially at the gate-level netlist where bugs introduced during synthesis are often missed by RTL-level verification due to longer simulation times. To address this, we introduce GraphFuzz, a graph-based hardware fuzzer designed for gate-level netlist verification. In this approach, hardware designs are modeled as graph nodes, with gate behaviors encoded as features. By leveraging graph learning algorithms, GraphFuzz efficiently detects hardware vulnerabilities by analyzing node patterns. Our evaluation across benchmark circuits and open-source processors demonstrates an average prediction accuracy of 80% and bug detection accuracy of 70%, highlighting the potential of graph-based methods for enhancing hardware verification.

Accelerating Hardware Verification with Graph Models

TL;DR

GraphFuzz, a graph-based hardware fuzzer designed for gate-level netlist verification, demonstrates an average prediction accuracy of 80% and bug detection accuracy of 70%, highlighting the potential of graph-based methods for enhancing hardware verification.

Abstract

The increasing complexity of modern processor and IP designs presents significant challenges in identifying and mitigating hardware flaws early in the IC design cycle. Traditional hardware fuzzing techniques, inspired by software testing, have shown promise but face scalability issues, especially at the gate-level netlist where bugs introduced during synthesis are often missed by RTL-level verification due to longer simulation times. To address this, we introduce GraphFuzz, a graph-based hardware fuzzer designed for gate-level netlist verification. In this approach, hardware designs are modeled as graph nodes, with gate behaviors encoded as features. By leveraging graph learning algorithms, GraphFuzz efficiently detects hardware vulnerabilities by analyzing node patterns. Our evaluation across benchmark circuits and open-source processors demonstrates an average prediction accuracy of 80% and bug detection accuracy of 70%, highlighting the potential of graph-based methods for enhancing hardware verification.

Paper Structure

This paper contains 38 sections, 6 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Proposed Work and Key Contributions:
  3. Background
  4. The Hardware Development Pipeline
  5. Hardware Fuzzing
  6. Hardware Fuzzer Characteristics:
  7. Fuzzing Beyond RTL: The Need for Post-Synthesis Fuzzing
  8. ML for Hardware Verification
  9. Graph Learning for IC Security
  10. Hardware Fuzzing with Proposed GraphFuzz
  11. Graph Generator
  12. Node Feature Encoding
  13. Graph Recurrent Neural Network
  14. EDA Dataset Generation
  15. NetGraph Fuzzer
  16. ...and 23 more sections

Figures (6)

  • Figure 1: Overview of hardware fuzzing, illustrating the selection of different parameters used in prior work and in our proposed GraphFuzz (highlighted in red)
  • Figure 2: Overview of our GraphFuzz Framework. Ⓐ Graph Generator (Section \ref{['graphgen']}), Ⓑ EDA Dataset Generation (Section \ref{['edadataset']}), Ⓒ GRNN Training (Section \ref{['grnn']}), Ⓓ GRNN Inference
  • Figure 3: Proposed Graph Node Encoding. GraphFuzz encodes three category of features : 1) Interface Type (Yellow) 2) Gate Type (Orange) and 3) Logic Value (Green)
  • Figure 4: Overview of Netgraph Fuzzer
  • Figure 5: Inference time of various architectures
  • ...and 1 more figures