An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks
Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial
Intelligence (AI) applications. Their ability to go beyond human precision has made these …
Intelligence (AI) applications. Their ability to go beyond human precision has made these …
A survey on modeling and improving reliability of DNN algorithms and accelerators
S Mittal - Journal of Systems Architecture, 2020 - Elsevier
As DNNs become increasingly common in mission-critical applications, ensuring their
reliable operation has become crucial. Conventional resilience techniques fail to account for …
reliable operation has become crucial. Conventional resilience techniques fail to account for …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
An efficient spiking neural network for recognizing gestures with a dvs camera on the loihi neuromorphic processor
R Massa, A Marchisio, M Martina… - 2020 International Joint …, 2020 - ieeexplore.ieee.org
Spiking Neural Networks (SNNs), the third generation NNs, have come under the spotlight
for machine learning based applications due to their biological plausibility and reduced …
for machine learning based applications due to their biological plausibility and reduced …
FT-CNN: Algorithm-based fault tolerance for convolutional neural networks
Convolutional neural networks (CNNs) are becoming more and more important for solving
challenging and critical problems in many fields. CNN inference applications have been …
challenging and critical problems in many fields. CNN inference applications have been …
Ft-clipact: Resilience analysis of deep neural networks and improving their fault tolerance using clipped activation
LH Hoang, MA Hanif, M Shafique - 2020 Design, Automation & …, 2020 - ieeexplore.ieee.org
Deep Neural Networks (DNNs) are widely being adopted for safety-critical applications, eg,
healthcare and autonomous driving. Inherently, they are considered to be highly error …
healthcare and autonomous driving. Inherently, they are considered to be highly error …
Soft errors in DNN accelerators: A comprehensive review
Deep learning tasks cover a broad range of domains and an even more extensive range of
applications, from entertainment to extremely safety-critical fields. Thus, Deep Neural …
applications, from entertainment to extremely safety-critical fields. Thus, Deep Neural …
Respawn: Energy-efficient fault-tolerance for spiking neural networks considering unreliable memories
Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …
unsupervised learning capabilities due to their biologically-inspired computation. However …
Securing deep spiking neural networks against adversarial attacks through inherent structural parameters
R El-Allami, A Marchisio, M Shafique… - … Design, Automation & …, 2021 - ieeexplore.ieee.org
Deep Learning (DL) algorithms have gained popularity owing to their practical problem-
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …