InterConFuzz: A Fuzzing-based Comprehensive NoC Verification Framework

Security verification of Network-on-Chip (NoC) systems is essential due to their intricate and high-concurrency structures. Traditional methods often fail to cover all scenarios or scale effectively, leading to prolonged verification and overlooked vulnerabilities. Our proposed solution, InterConFuzz, a hybrid hardware fuzzing technique, uses symbolic execution for extensive coverage. Developed on Universal Verification Methodology (UVM), InterConFuzz discovered five security flaws in the NoC architecture of the OpenTitan SoC-surpassing existing techniques by three-while reducing memory and computational needs by 24.4% and 29.5%, respectively. Furthermore, InterConFuzz furnished comparable functional coverage compared to existing NoC fuzzing approaches, proving its efficiency and robustness.

Authors

Institutions

Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133216
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

InterConFuzz: A Fuzzing-based Comprehensive NoC Verification Framework

Hyunmin Kim, Kanad Basu, Samit Shahnawaz Miftah
Advanced Memory and Neural Computing
article

InterConFuzz: A Fuzzing-based Comprehensive NoC Verification Framework

Hyunmin Kim, Kanad Basu, Samit Shahnawaz Miftah
article en

Abstract

Security verification of Network-on-Chip (NoC) systems is essential due to their intricate and high-concurrency structures. Traditional methods often fail to cover all scenarios or scale effectively, leading to prolonged verification and overlooked vulnerabilities. Our proposed solution, InterConFuzz, a hybrid hardware fuzzing technique, uses symbolic execution for extensive coverage. Developed on Universal Verification Methodology (UVM), InterConFuzz discovered five security flaws in the NoC architecture of the OpenTitan SoC-surpassing existing techniques by three-while reducing memory and computational needs by 24.4% and 29.5%, respectively. Furthermore, InterConFuzz furnished comparable functional coverage compared to existing NoC fuzzing approaches, proving its efficiency and robustness.

The University of Texas at Dallas (US), Technology Innovation Institute (AE), University of North Texas at Dallas (US)
Openalex Percentile: Top 76%
Advanced Memory and Neural Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

InterConFuzz: A Fuzzing-based Comprehensive NoC Verification Framework — Hyunmin Kim, Kanad Basu, et al. · (2025) | TGRS Research Map | TGRS