224 citations · 224 across the 7 of their papers we have counts for
8 papers
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…
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,…
Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression
Sara Makenali, Babak Rokh, Ali Azarpeyvand
Deep Neural Networks (DNNs) have achieved significant advances in a wide range of applications. However, their deployment on resource-constrained devices remains a challenge due to…
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.
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification
Babak Rokh, Ali Azarpeyvand, Alireza Khanteymoori
Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of…