27 citations · 47 across the 8 of their papers we have counts for
9 papers
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…
NOM: Network-On-Memory for Inter-Bank Data Transfer in Highly-Banked Memories
Seyyed Hossein SeyyedAghaei Rezaei, Mehdi Modarressi, Rachata Ausavarungnirun +3
Data copy is a widely-used memory operation in many programs and operating system services. In conventional computers, data copy is often carried out by two separate read and write…
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…
MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification
Seyed Ahmad Mirsalari, Sima Sinaei, Mostafa E. Salehi +1
Recurrent Neural Networks (RNN) are widely used for learning sequences in applications such as EEG classification. Complex RNNs could be hardly deployed on wearable devices due to…
Computing with hardware neurons: spiking or classical? Perspectives of applied Spiking Neural Networks from the hardware side
Sergei Dytckov, Masoud Daneshtalab
While classical neural networks take a position of a leading method in the machine learning community, spiking neuromorphic systems bring attention and large projects in neuroscien…