26 citations · 39 across the 7 of their papers we have counts for
7 papers
CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation
Matthew DeLorenzo, Vasudev Gohil, Jeyavijayan Rajendran
Large Language Models (LLMs) have proved effective and efficient in generating code, leading to their utilization within the hardware design process. Prior works evaluating LLMs' a…
Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing
Mohamadreza Rostami, Marco Chilese, Shaza Zeitouni +3
Modern computing systems heavily rely on hardware as the root of trust. However, their increasing complexity has given rise to security-critical vulnerabilities that cross-layer at…
WhisperFuzz: White-Box Fuzzing for Detecting and Locating Timing Vulnerabilities in Processors
Pallavi Borkar, Chen Chen, Mohamadreza Rostami +5
Timing vulnerabilities in processors have emerged as a potent threat. As processors are the foundation of any computing system, identifying these flaws is imperative. Recently fuzz…
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning
Vasudev Gohil, Satwik Patnaik, Dileep Kalathil +1
Machine learning has shown great promise in addressing several critical hardware security problems. In particular, researchers have developed novel graph neural network (GNN)-based…
Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS
Matthew DeLorenzo, Animesh Basak Chowdhury, Vasudev Gohil +4
Existing large language models (LLMs) for register transfer level code generation face challenges like compilation failures and suboptimal power, performance, and area (PPA) effici…
PSOFuzz: Fuzzing Processors with Particle Swarm Optimization
Chen Chen, Vasudev Gohil, Rahul Kande +2
Hardware security vulnerabilities in computing systems compromise the security defenses of not only the hardware but also the software running on it. Recent research has shown that…