In this paper, we evaluate the impact on reliability and performance of the selective approximation of Convolutional Neural Networks (CNNs) layers on NVIDIA mixed-precision architectures. We found that, even without affecting accuracy, the approximation from single to half precision of each layer has a different impact on both performance and output error.
Impact of Layers Selective Approximation on CNNs Reliability and Performance / Rech Junior, Rubens Luiz; Rech, Paolo. - (2020), pp. 1-4. (Intervento presentato al convegno DFT 2020 tenutosi a Frascati, Italy (On-line Virtual Event) nel 19th–21st October 2020) [10.1109/DFT50435.2020.9250821].
Impact of Layers Selective Approximation on CNNs Reliability and Performance
Rech, PaoloUltimo
2020-01-01
Abstract
In this paper, we evaluate the impact on reliability and performance of the selective approximation of Convolutional Neural Networks (CNNs) layers on NVIDIA mixed-precision architectures. We found that, even without affecting accuracy, the approximation from single to half precision of each layer has a different impact on both performance and output error.| File | Dimensione | Formato | |
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DFT_Impact_of_Layers_Selective_Approximation_on_CNNs_Reliability_and_Performance.pdf
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