7 papers
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…
DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural Network
Mohammad Hasan Ahmadilivani, Jaan Raik, Masoud Daneshtalab +1
The growing exploitation of Machine Learning (ML) in safety-critical applications necessitates rigorous safety analysis. Hardware reliability assessment is a major concern with res…
ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs
Seyedhamidreza Mousavi, Mohammad Hasan Ahmadilivani, Jaan Raik +2
Deep Neural Networks (DNNs) are extensively employed in safety-critical applications where ensuring hardware reliability is a primary concern. To enhance the reliability of DNNs ag…