RL-TPG: Automated Pre-Silicon Security Verification through Reinforcement Learning-Based Test Pattern Generation
Nurun Nahar Mondol, Arash Vafei, Kimia Zamiri Azar, Farimah Farahmandi, Mark Mohammad Tehranipoor · 2024
Verifying the security of System-on-Chip (SoC) designs against hardware vulnerabilities is challenging because of the increasing complexity of SoCs, the diverse sources of vulnerabilities, and the need for comprehensive testing to identify potential security threats. In this paper, we propose RL-TPG, a novel framework that combines traditional verification with hardware security verification using Reinforcement Learning (RL) in Register Transfer Level (RTL) design. Significant research has been done on formal verification, semi-formal verification, automated security asset identification, and gate-level netlist. However, the area of automated simulation using machine learning at RTL is still unexplored. RL-TPG employs an RL agent that generates intelligent test patterns targeting security properties, verification coverage, and rare nodes of the design to achieve security property violation, increase verification coverage, and reach rare nodes. Our framework triggers all embedded vulnerabilities, achieving an average of 90% traditional coverage in an average of 192 seconds for the experimental benchmarks. To demonstrate the effectiveness of the approach, the results are compared with JasperGold by Cadence.