55 citations · 66 across the 10 of their papers we have counts for
4 papers · 1 filter
Attacking Deep Learning AI Hardware with Universal Adversarial Perturbation
Mehdi Sadi, B. M. S. Bahar Talukder, Kaniz Mishty +1
Universal Adversarial Perturbations are image-agnostic and model-independent noise that when added with any image can mislead the trained Deep Convolutional Neural Networks into th…
Designing Efficient and High-performance AI Accelerators with Customized STT-MRAM
Kaniz Mishty, Mehdi Sadi
In this paper, we demonstrate the design of efficient and high-performance AI/Deep Learning accelerators with customized STT-MRAM and a reconfigurable core. Based on model-driven d…
True Random Number Generation using Latency Variations of Commercial MRAM Chips
Farah Ferdaus, B. M. S. Bahar Talukder, Mehdi Sadi +1
The emerging magneto-resistive RAM (MRAM) has considerable potential to become a universal memory technology because of its several advantages: unlimited endurance, lower read/writ…
Special Session: Reliability Analysis for ML/AI Hardware
Shamik Kundu, Kanad Basu, Mehdi Sadi +4
Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many c…