A systematic literature review on hardware reliability assessment methods for deep neural networks
Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be
utilized in various applications due to their capability to learn how to solve complex …
utilized in various applications due to their capability to learn how to solve complex …
Artificial neural networks for space and safety-critical applications: Reliability issues and potential solutions
P Rech - IEEE Transactions on Nuclear Science, 2024 - ieeexplore.ieee.org
Machine learning is among the greatest advancements in computer science and
engineering and is today used to classify or detect objects, a key feature in autonomous …
engineering and is today used to classify or detect objects, a key feature in autonomous …
High energy and thermal neutron sensitivity of google tensor processing units
RLR Junior, S Malde, C Cazzaniga… - … on Nuclear Science, 2022 - ieeexplore.ieee.org
In this article, we investigate the reliability of Google's coral tensor processing units (TPUs)
to both high-energy atmospheric neutrons (at ChipIR) and thermal neutrons from a pulsed …
to both high-energy atmospheric neutrons (at ChipIR) and thermal neutrons from a pulsed …
Exploring hardware fault impacts on different real number representations of the structural resilience of tcus in gpus
The most recent generations of graphics processing units (GPUs) boost the execution of
convolutional operations required by machine learning applications by resorting to …
convolutional operations required by machine learning applications by resorting to …
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 …
Numerical behavior of NVIDIA tensor cores
We explore the floating-point arithmetic implemented in the NVIDIA tensor cores, which are
hardware accelerators for mixed-precision matrix multiplication available on the Volta …
hardware accelerators for mixed-precision matrix multiplication available on the Volta …
Deepvigor: Vulnerability value ranges and factors for dnns' reliability assessment
Deep Neural Networks (DNNs) and their accelerators are being deployed ever more
frequently in safety-critical applications leading to increasing reliability concerns. A …
frequently in safety-critical applications leading to increasing reliability concerns. A …
Exploration of activation fault reliability in quantized systolic array-based dnn accelerators
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 …
stand along with the need for reducing the computational burden on the hardware platforms …
Analyzing the impact of different real number formats on the structural reliability of tcus in gpus
1 Modern Graphics Processing Units (GPUs) boost the execution of tiled matrix
multiplications by extensively using in-chip accelerators (Tensor Core Units or TCUs) …
multiplications by extensively using in-chip accelerators (Tensor Core Units or TCUs) …
On the rise of amd matrix cores: Performance, power efficiency, and programmability
G Schieffer, DA De Medeiros, J Faj… - … Analysis of Systems …, 2024 - ieeexplore.ieee.org
Matrix multiplication is a core computational part of deep learning and scientific workloads.
The emergence of Matrix Cores in high-end AMD GPUs, a building block of Exascale …
The emergence of Matrix Cores in high-end AMD GPUs, a building block of Exascale …