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 …
Testability and dependability of AI hardware: Survey, trends, challenges, and perspectives
F Su, C Liu, HG Stratigopoulos - IEEE Design & Test, 2023 - ieeexplore.ieee.org
Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …
underlying technologies than those used in traditional digital processors or logic circuits …
Dependable dnn accelerator for safety-critical systems: A review on the aging perspective
In the modern era, artificial intelligence (AI) and deep learning (DL) seamlessly integrate into
various spheres of our daily lives. These cutting-edge disciplines have given rise to …
various spheres of our daily lives. These cutting-edge disciplines have given rise to …
Toward functional safety of systolic array-based deep learning hardware accelerators
High accuracy and ever-increasing computing power have made deep neural networks
(DNNs) the algorithm of choice for various machine learning, computer vision, and image …
(DNNs) the algorithm of choice for various machine learning, computer vision, and image …
Trouble-shooting at gan point: Improving functional safety in deep learning accelerators
The proliferation of Deep Neural Networks (DNNs) in real-time mission critical applications
has promoted the implementation of custom-built DNN inference accelerators. These …
has promoted the implementation of custom-built DNN inference accelerators. These …
A survey on failure analysis and fault injection in AI systems
The rapid advancement of Artificial Intelligence (AI) has led to its integration into various
areas, especially with Large Language Models (LLMs) significantly enhancing capabilities …
areas, especially with Large Language Models (LLMs) significantly enhancing capabilities …
Neuron fault tolerance in spiking neural networks
The error-resiliency of Artificial Intelligence (AI) hardware accelerators is a major concern,
especially when they are deployed in mission-critical and safety-critical applications. In this …
especially when they are deployed in mission-critical and safety-critical applications. In this …
Soft error tolerant convolutional neural networks on FPGAs with ensemble learning
Z Gao, H Zhang, Y Yao, J Xiao, S Zeng… - … Transactions on Very …, 2022 - ieeexplore.ieee.org
Convolutional neural networks (CNNs) are widely used in computer vision and natural
language processing. Field-programmable gate arrays (FPGAs) are popular accelerators for …
language processing. Field-programmable gate arrays (FPGAs) are popular accelerators for …
Saca-FI: A microarchitecture-level fault injection framework for reliability analysis of systolic array based CNN accelerator
J Tan, Q Wang, K Yan, X Wei, X Fu - Future Generation Computer Systems, 2023 - Elsevier
As convolutional neural network CNN accelerators are being adopted in emerging safety-
critical areas, their reliability becomes prominent. The systolic array is widely used as the …
critical areas, their reliability becomes prominent. The systolic array is widely used as the …
Reliability evaluation and analysis of FPGA-based neural network acceleration system
Prior works typically conducted the fault analysis of neural network accelerator computing
arrays with simulation and focused on the prediction accuracy loss of the neural network …
arrays with simulation and focused on the prediction accuracy loss of the neural network …