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, R. L.; Rech, P.. - (2020), pp. 1-4. (Intervento presentato al convegno 33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2020 tenutosi a ita nel 2020) [10.1109/DFT50435.2020.9250821].
Impact of Layers Selective Approximation on CNNs Reliability and Performance
Rech P.
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
