6 papers
BARE-AI: Bit-Flip Attack Resilience in AI Hardware through Built-in Performance Monitors
Habibur Rahaman, Swastik Bhattacharya, Sanjay Das +2
Deep Neural Networks (DNNs) are integral to many safety critical systems, yet they remain highly vulnerable to bit-flip attacks (BFAs), where a few memory level perturbations can d…
COBRA: Catastrophic Bit-flip Reliability Analysis of State-Space Models
Sanjay Das, Swastik Bhattacharya, Shamik Kundu +3
State-space models (SSMs), exemplified by the Mamba architecture, have recently emerged as state-of-the-art sequence-modeling frameworks, offering linear-time scalability together…
SafeCiM: Investigating Resilience of Hybrid Floating-Point Compute-in-Memory Deep Learning Accelerators
Swastik Bhattacharya, Sanjay Das, Anand Menon +3
Deep Neural Networks (DNNs) continue to grow in complexity with Large Language Models (LLMs) incorporating vast numbers of parameters. Handling these parameters efficiently in trad…
Enhancing Large Language Models for Hardware Verification: A Novel SystemVerilog Assertion Dataset
Anand Menon, Samit S Miftah, Shamik Kundu +7
Hardware verification is crucial in modern SoC design, consuming around 70% of development time. SystemVerilog assertions ensure correct functionality. However, existing industrial…
GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs
Sanjay Das, Swastik Bhattacharya, Souvik Kundu +4
Large Language Models (LLMs) have revolutionized natural language processing (NLP), excelling in tasks like text generation and summarization. However, their increasing adoption in…
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…