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- W4379115523 abstract "Nowadays, Deep Neural Networks (DNNs) are one of the most computationally-intensive algorithms because of the (i) huge amount of data to be transferred from/to the memory, and (ii) the huge amount of matrix multiplications to compute. These issues motivate the design of custom DNN hardware accelerators. These accelerators are widely used for low-latency safety-critical applications such as object detection in autonomous cars. Safety-critical applications have to be resilient with respect to hardware faults and Deep Learning (DL) accelerators are subjected to hardware faults that can cause functional failures, potentially leading to catastrophic consequences. Although DNNs possess a certain level of intrinsic resilience, it varies depending on the hardware on which they are run. The intent of the paper is to assess the resilience of a systolic-array-based DNN accelerator in the presence of hardware faults, in order to identify the architectural parameters that may mainly impact the DNN resilience." @default.
- W4379115523 created "2023-06-03" @default.
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- W4379115523 date "2023-05-03" @default.
- W4379115523 modified "2023-10-18" @default.
- W4379115523 title "Resilience-Performance Tradeoff Analysis of a Deep Neural Network Accelerator" @default.
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- W4379115523 doi "https://doi.org/10.1109/ddecs57882.2023.10139704" @default.
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