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Found 6 papers, 0 papers with code
CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation
Large Language Models (LLMs) have proved effective and efficient in generating code, leading to their utilization within the hardware design process.
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning
We target five GNN-based techniques for four crucial classes of problems in hardware security: IP piracy, detecting/localizing HTs, reverse engineering, and hardware obfuscation.
Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS
Existing large language models (LLMs) for register transfer level code generation face challenges like compilation failures and suboptimal power, performance, and area (PPA) efficiency.
Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges
In this brief, we outline the development of RL agents in detecting hardware Trojans, one of the most challenging hardware security problems.
DETERRENT: Detecting Trojans using Reinforcement Learning
Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat.
ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning
Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures.
