17 citations · 58 across the 29 of their papers we have counts for
6 papers · 1 filter
Carry-Through Checksum: A Lightweight Fault-Detection for CNN Inference at the Edge
Kyrylo Nazarevych, Mohammad Hasan Ahmadilivani, Krister Kaldre +2
Convolutional Neural Networks (CNNs) are increasingly deployed in safety-critical edge applications, where soft errors can silently corrupt inference outputs and lead to unsafe dec…
ANTMAN: An Efficient and Interpretable RTL-Level Run-Time Detection Framework for Stealthy Branch Predictor Attacks on BOOM
Muhammad Hassan, Maria Mushtaq, Sharjeel Imtiaz +2
Runtime detection of microarchitectural side channel attacks remains significantly underexplored in RISCV compared with x86 and ARM ISAs, posing a serious threat to critical applic…
CheckOne: Lightweight Fault Detection and Mitigation for Vision Transformers
Mohammad Hasan Ahmadilivani, Sven-Markus Loorits, Jaan Raik
The wide adoption of Vision Transformers (ViTs) in safety-critical applications raises reliability concerns related to hardware faults. Algorithm-Based Fault Tolerance (ABFT) metho…
CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks
Bahram Parchekani, Samira Nazari, Ali Azarpeyvand +3
Deep Neural Networks (DNNs) used in safety-critical applications are vulnerable to hardware and memory faults that corrupt network weights and degrade reliability. In this paper, w…
Effective and Memory-Efficient Alternatives to ECC for Reliable Large-Scale DNNs
Mohammad Hasan Ahmadilivani, Marten Roots, Marco Restifo +3
Modern Deep Learning (DL) workloads are increasingly deployed in safety-critical domains, such as automotive systems and hyperscale data centers, where transient hardware faults po…
Cross-Layer Co-Optimized LSTM Accelerator for Real-Time Gait Analysis
Mohammad Hasan Ahmadilivani, Levent Aksoy, Mohammad Eslami +2
Long Short-Term Memory (LSTM) neural networks have penetrated healthcare applications where real-time requirements and edge computing capabilities are essential. Gait analysis that…