most citedSALAD: Systematic Assessment of Machine Unlearning on LLM-Aided Hardware Design

1 citations · 2 across the 5 of their papers we have counts for

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cs.CR2025

VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding

Zeng Wang, Minghao Shao, Mohammed Nabeel +7

Large language models (LLMs) offer significant potential for coding, yet fine-tuning (FT) with curated data is essential for niche languages like Verilog. Using proprietary intelle…

cs.CR2024

Lightweight Countermeasures Against Static Power Side-Channel Attacks

Jitendra Bhandari, Mohammed Nabeel, Likhitha Mankali +3

This paper presents a novel defense strategy against static power side-channel attacks (PSCAs), a critical threat to cryptographic security. Our method is based on (1) carefully tu…