11 papers
Fusion: A Framework for Unified Sequential Token AdaptatIon in VisiOn TraNsformers
Aravind Pradeep, Samira Nazari, Mahdi Taheri +1
Vision Transformers achieve strong image classification accuracy but process all image regions with nearly the same computation, even when many regions are redundant or uninformati…
HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning
Amir Hossein Shahdadian, Ahmed M. Abdelmoniem, Mahdi Taheri +2
Edge services increasingly use federated learning to personalize on-device models while keeping sensitive data local. In practice, deployments must handle heterogeneity in both cli…
SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors
Pramit Kumar Bhaduri, Mahdi Taheri, Samira Nazari +3
With the growth of Vision Transformers in safety-critical domains like autonomous systems and medical imaging, ensuring their reliability against soft errors is paramount. While Vi…
Mix-and-Match Pruning: Globally Guided Layer-Wise Sparsification of DNNs
Danial Monachan, Samira Nazari, Mahdi Taheri +4
Deploying deep neural networks (DNNs) on edge devices requires strong compression with minimal accuracy loss. This paper introduces Mix-and-Match Pruning, a globally guided, layer-…
RESQ: A Unified Framework for REliability- and Security Enhancement of Quantized Deep Neural Networks
Ali Soltan Mohammadi, Samira Nazari, Ali Azarpeyvand +5
This work proposes a unified three-stage framework that produces a quantized DNN with balanced fault and attack robustness. The first stage improves attack resilience via fine-tuni…
SPARQ: Spiking Early-Exit Neural Networks for Energy-Efficient Edge AI
Parth Patne, Mahdi Taheri, Ali Mahani +3
Spiking neural networks (SNNs) offer inherent energy efficiency due to their event-driven computation model, making them promising for edge AI deployment. However, their practical…