5 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…
Post-Training Quantization of Autoregressive Weather Models
Ananyo Bhattacharya, Swastik Bhattacharya, Christiane Jablonowski
Advancements in high-resolution numerical weather prediction (NWP) and data assimilation (DA) have shaped the developments in deep learning (DL) architectures emulating atmospheric…
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…
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…