27 citations · 47 across the 8 of their papers we have counts for
6 papers · 1 filter
Enhancing Fault Resilience of QNNs by Selective Neuron Splitting
Mohammad Hasan Ahmadilivani, Mahdi Taheri, Jaan Raik +2
The superior performance of Deep Neural Networks (DNNs) has led to their application in various aspects of human life. Safety-critical applications are no exception and impose rigo…
Special Session: Approximation and Fault Resiliency of DNN Accelerators
Mohammad Hasan Ahmadilivani, Mario Barbareschi, Salvatore Barone +9
Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this co…
APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors
Mahdi Taheri, Mohammad Hasan Ahmadilivani, Maksim Jenihhin +2
Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in safety-critical applications raises new reliability concerns. In practice, methods for fault injection by emu…
DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
Mahdi Taheri, Mohammad Riazati, Mohammad Hasan Ahmadilivani +5
While the role of Deep Neural Networks (DNNs) in a wide range of safety-critical applications is expanding, emerging DNNs experience massive growth in terms of computation power. I…
DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment
Mohammad Hasan Ahmadilivani, Mahdi Taheri, Jaan Raik +2
Deep Neural Networks (DNNs) and their accelerators are being deployed ever more frequently in safety-critical applications leading to increasing reliability concerns. A traditional…
Multi-level Binarized LSTM in EEG Classification for Wearable Devices
Najmeh Nazari, Seyed Ahmad Mirsalari, Sima Sinaei +2
Long Short-Term Memory (LSTM) is widely used in various sequential applications. Complex LSTMs could be hardly deployed on wearable and resourced-limited devices due to the huge am…