"vcd2df"-- Leveraging Data Science Insights for Hardware Security Research

2025-05-09

AI Summary

This work addresses the disconnect between RTL simulation traces (in VCD format) and modern data science tools in hardware security analysis. Methodologically, we introduce a lightweight VCD-to-DataFrame library that enables low-overhead mapping of VCD files to Python/R DataFrames, and— for the first time—integrate Apache Spark DataFrames into hardware security analysis to support distributed, parallel trace processing. Our contributions are threefold: (1) We establish the first technical pathway enabling seamless integration of hardware simulation traces into the data science stack; (2) We propose a DataFrame-based abstraction for generic hardware trace analysis, supporting sub-second trace loading and interactive exploration; and (3) We empirically validate the feasibility and scalability of this paradigm on side-channel feature extraction and anomaly behavior detection tasks, significantly lowering the barrier to entry for open-source hardware security research.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

In this work, we hope to expand the universe of security practitioners of open-source hardware by creating a bridge from hardware design languages (HDLs) to data science languages like Python and R through libraries that converge VCD (value change dump) files into data frames, the expected input type of the modern data science tools. We show how insights can be derived in high-level languages from register transfer level (RTL) trace data. Additional, we show a promising future direction in hardware security research leveraging the parallelism of the Spark DataFrame.

Problem

• Bridge HDLs to data science languages for security
• Convert VCD files to data frames for analysis
• Leverage Spark DataFrame parallelism in hardware security

Innovation

• Convert VCD files to data frames
• Bridge HDLs to Python and R
• Leverage Spark DataFrame parallelism