10 papers
Client-Aware Security Adaptive Early Exit Vision Transformers for Heterogeneous Federated Learning
Sahar Refaei Ghalibafan, Samira Nazari, Christian Herglotz +2
Federated learning (FL) trains a shared model across data-owning clients without centralizing raw data, but client data difficulty and compute budgets can differ. We study local ea…
REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration
Mahdi Taheri, Samira Nazari, Mubassher Ansari +4
Efficient deployment of Deep Neural Networks (DNNs) on edge accelerators requires aggressive model compression while maintaining reliability in fault-prone hardware environments. T…
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…
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…
HAWX: A Hardware-Aware FrameWork for Fast and Scalable ApproXimation of DNNs
Samira Nazari, Mohammad Saeed Almasi, Mahdi Taheri +4
This work presents HAWX, a hardware-aware scalable exploration framework that employs multi-level sensitivity scoring at different DNN abstraction levels (operator, filter, layer,…
AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators
Mahdi Taheri, Natalia Cherezova, Samira Nazari +6
In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators.