Reliability exploration of system-on-chip with multi-bit-width accelerator for multi-precision deep neural networks
Deep neural networks (DNNs) in safety-critical applications demand high reliability even
when running on edge-computing devices. Recent works on System-on-Chip (SoC) design …
when running on edge-computing devices. Recent works on System-on-Chip (SoC) design …
Characterizing a neutron-induced fault model for deep neural networks
FF Dos Santos, A Kritikakou… - … on Nuclear Science, 2022 - ieeexplore.ieee.org
The reliability evaluation of deep neural networks (DNNs) executed on graphic processing
units (GPUs) is a challenging problem, since the hardware architecture is highly complex …
units (GPUs) is a challenging problem, since the hardware architecture is highly complex …
HyCA: A hybrid computing architecture for fault-tolerant deep learning
Hardware faults on the regular 2-D computing array of a typical deep learning accelerator
(DLA) can lead to dramatic prediction accuracy loss. Prior redundancy design approaches …
(DLA) can lead to dramatic prediction accuracy loss. Prior redundancy design approaches …
FTT-NAS: Discovering fault-tolerant neural architecture
With the fast evolvement of deep-learning specific embedded computing systems,
applications powered by deep learning are moving from the cloud to the edge. When …
applications powered by deep learning are moving from the cloud to the edge. When …
Precision-aware latency and energy balancing on multi-accelerator platforms for dnn inference
The need to execute Deep Neural Networks (DNNs) at low latency and low power at the
edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) …
edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) …
TEA-DNN: the quest for time-energy-accuracy co-optimized deep neural networks
Embedded deep learning platforms have witnessed two simultaneous improvements. First,
the accuracy of convolutional neural networks (CNNs) has been significantly improved …
the accuracy of convolutional neural networks (CNNs) has been significantly improved …
Hardware-aware neural architecture search for stochastic computing-based neural networks on tiny devices
Along with the progress of artificial intelligence (AI) democratization, there is an increasing
potential for the deployment of deep neural networks (DNNs) to tiny devices, such as …
potential for the deployment of deep neural networks (DNNs) to tiny devices, such as …
Evaluation of algorithm-based fault tolerance for machine learning and computer vision under neutron radiation
S Roffe, AD George - 2020 IEEE Aerospace Conference, 2020 - ieeexplore.ieee.org
In the past decade, there has been a push for deployment of commercial-off-the-shelf
(COTS) avionics due in part to cheaper costs and the desire for more performance …
(COTS) avionics due in part to cheaper costs and the desire for more performance …
Sensitivity-based error resilient techniques with heterogeneous multiply–accumulate unit for voltage scalable deep neural network accelerators
With inherent algorithmic error resilience of deep neural networks (DNNs), supply voltage
scaling could be a promising technique for energy efficient DNN accelerator design. In this …
scaling could be a promising technique for energy efficient DNN accelerator design. In this …
Uncertainty modeling of emerging device based computing-in-memory neural accelerators with application to neural architecture search
emerging device based Computing-in-memory (CiM) has been proved to be a promising
candidate for high energy efficiency deep neural network (DNN) computations. However …
candidate for high energy efficiency deep neural network (DNN) computations. However …