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 …
Deep learning based image classification for embedded devices: A systematic review
Deep learning models are widely employed to solve complex problems in different areas,
particularly for image classification, because of their high performance in pattern recognition …
particularly for image classification, because of their high performance in pattern recognition …
On the dependability of bidirectional encoder representations from transformers (BERT) to soft errors
Z Gao, Z Yin, J Wang, R Su, J Deng… - IEEE Transactions …, 2025 - ieeexplore.ieee.org
Transformers are widely used in natural language processing and computer vision, and
Bidirectional Encoder Representations from Transformers (BERT) is one of the most popular …
Bidirectional Encoder Representations from Transformers (BERT) is one of the most popular …
Resilience of Deep Learning applications: a systematic survey of analysis and hardening techniques
Machine Learning (ML) is currently being exploited in numerous applications being one of
the most effective Artificial Intelligence (AI) technologies, used in diverse fields, such as …
the most effective Artificial Intelligence (AI) technologies, used in diverse fields, such as …
Systematic reliability evaluation of FPGA implemented CNN accelerators
Convolutional neural networks (CNN) have become essential for many scientific and
industrial applications, such as image classification and pattern detection. Among the …
industrial applications, such as image classification and pattern detection. Among the …
Assessing the reliability of FPGA-Based Quantised Neural Networks under Neutron Irradiation
I Souvatzoglou, D Agiakatsikas… - … on Nuclear Science, 2024 - ieeexplore.ieee.org
SRAM field-programmable gate arrays (FPGAs) are popular computing platforms for
implementing neural networks (NNs) due to their flexibility and low recurring engineering …
implementing neural networks (NNs) due to their flexibility and low recurring engineering …
Detect and Replace: Efficient Soft Error Protection of FPGA-Based CNN Accelerators
Z Gao, Y Qi, J Shi, Q Liu, G Ge, Y Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Convolutional neural networks (CNNs) are widely used in computer vision and natural
language processing. Field-programmable gate arrays (FPGAs) are a popular accelerator …
language processing. Field-programmable gate arrays (FPGAs) are a popular accelerator …
Assessing the Reliability of Different Split Computing Neural Network Applications
1 Neural Networks (NNs) are increasingly adopted in many domains. Given their
considerable computational expenses required only during inference, it has been proposed …
considerable computational expenses required only during inference, it has been proposed …
The Rapid and Accurate Detection of Kidney Bean Seeds Based on a Compressed Yolov3 Model
Y Wang, H Bai, L Sun, Y Tang, Y Huo, R Min - Agriculture, 2022 - mdpi.com
Due to their rich nutritional value, kidney beans are considered one of the major products of
international agricultural trade. The conventional method used for the manual detection of …
international agricultural trade. The conventional method used for the manual detection of …
Tradeoff Between Performance and Reliability in FPGA Accelerated DNNs for Space Applications
The space industry is interested in using Commercial-off-the-self (COTS) SRAM Field
Programmable Gate Arrays (FPGAs) for Deep Neural Networks (DNN) inference …
Programmable Gate Arrays (FPGAs) for Deep Neural Networks (DNN) inference …