most citedUnlocking Hardware Security Assurance: The Potential of LLMs

16 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

quant-ph2024

PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud

Zhepeng Wang, Yi Sheng, Nirajan Koirala +4

Benefiting from cloud computing, today's early-stage quantum computers can be remotely accessed via the cloud services, known as Quantum-as-a-Service (QaaS). However, it poses a hi…

cs.LG2024

Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning

Ayush Arunachalam, Ian Kintz, Suvadeep Banerjee +7

Given the widespread use of safety-critical applications in the automotive field, it is crucial to ensure the Functional Safety (FuSa) of circuits and components within automotive…

cs.CR2023

SCAR: Power Side-Channel Analysis at RTL-Level

Amisha Srivastava, Sanjay Das, Navnil Choudhury +4

Power side-channel attacks exploit the dynamic power consumption of cryptographic operations to leak sensitive information of encryption hardware. Therefore, it is necessary to con…

cs.ET2023

QuBEC: Boosting Equivalence Checking for Quantum Circuits with QEC Embedding

Chao Lu, Navnil Choudhury, Utsav Banerjee +2

Quantum computing has proven to be capable of accelerating many algorithms by performing tasks that classical computers cannot. Currently, Noisy Intermediate Scale Quantum (NISQ) m…

cs.CR202316 cited

Unlocking Hardware Security Assurance: The Potential of LLMs

Xingyu Meng, Amisha Srivastava, Ayush Arunachalam +5

System-on-Chips (SoCs) form the crux of modern computing systems. SoCs enable high-level integration through the utilization of multiple Intellectual Property (IP) cores. However,…