Tanvir Rahman

Tanvir Rahman, Shuvagata Saha, Ahmed Y. Alhurubi et al.

Increasing system-on-chip (SoC) heterogeneity, deep hardware/software integration, and the proliferation of third-party intellectual property (IP) have brought security validation to the forefront of semiconductor design. While simulation and formal verification remain indispensable, they often struggle to expose vulnerabilities that emerge only under realistic execution conditions, long software-driven interactions, and adversarial stimuli. In this context, hardware emulation is emerging as an increasingly important pre-silicon verification technology because it enables higher-throughput execution of RTL designs under realistic hardware/software workloads while preserving sufficient fidelity for security-oriented analysis. This paper presents a comprehensive survey and perspective on emulation-based security verification and validation. We organize the landscape of prior work across assertion-based security checking, coverage-driven exploration, adversarial testing, information-flow tracking, fault injection, and side-channel-oriented evaluation. We provide a structured view of emulation-enabled security verification workflows, including instrumentation, stimulus generation, runtime monitoring, and evidence-driven analysis. We also examine practical challenges related to observability, scalability, property specification, and the definition of security-oriented coverage metrics for emulation-based verification. Finally, we discuss emerging directions such as AI-assisted emulation, digital security twins, chiplet-scale security exploration, automated vulnerability assessment, and cloud-scale secure emulation. Overall, this paper positions emulation as a promising foundation for the next generation of pre-silicon hardware security assurance.

Paul E. Calzada, Zahin Ibnat, Tanvir Rahman et al.

Large Language Models (LLMs) are gaining popularity for hardware design automation, particularly through Register Transfer Level (RTL) code generation. In this work, we examine the current literature on RTL generation using LLMs and identify key requirements for training and fine-tuning datasets. We construct a robust Verilog dataset through an automated three-pronged process involving database (DB) creation and management with PostgreSQL, data collection from code hosting sites like OpenCores and GitHub, and data preprocessing to verify the codes' syntax, run logic synthesis, and extract relevant module metadata. We implement a scalable and efficient DB infrastructure to support analysis and detail our preprocessing pipeline to enforce high-quality data before DB insertion. The resulting dataset comprises 20,392 Verilog samples, 751 MB of Verilog code data, which is the largest high-quality Verilog dataset for LLM fine-tuning to our knowledge. We further evaluate the dataset, address associated challenges, and explore potential applications for future research and development in LLM-based hardware generation.