2021
DOI: 10.32985/ijeces.12.2.4
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Analyzing the Resilience of Convolutional Neural Networks Implemented on GPUs

Abstract: There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications. Presently, GPUs are the most prominent and dominated DNN accelerators to increase the execution speed of CNN algorithms to improve their performance as well as the Latency. However, GPUs are prone to soft errors. These errors can impact the behaviors of the GPU dramatically. Thus, the generated fault may corrupt data values or logic operations and cause errors, such as Silent Data Corruption. unfortunately, sof… Show more

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Cited by 3 publications
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