17 citations · 58 across the 29 of their papers we have counts for
6 papers · 1 filter
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…
Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning
Mohammad Hasan Ahmadilivani, Seyedhamidreza Mousavi, Jaan Raik +2
Convolutional Neural Networks (CNNs) have become integral in safety-critical applications, thus raising concerns about their fault tolerance. Conventional hardware-dependent fault…
SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators
Mahdi Taheri, Masoud Daneshtalab, Jaan Raik +5
Systolic array has emerged as a prominent architecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essential for depl…
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.
Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators
Mahdi Taheri, Natalia Cherezova, Mohammad Saeed Ansari +4
The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability stand along with the need for reducing the computational burden on the hardware platforms,…