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.
33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2020
usa
Institute of Electrical and Electronics Engineers Inc.
978-1-7281-9457-8
Rech, R. L.; Rech, P.
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/346645
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