How Reduced Data Precision and Degree of Parallelism Impact the Reliability of Convolutional Neural Networks on FPGAs

Convolutional neural networks (CNNs) are becoming attractive alternatives to traditional image-processing algorithms in self-driving vehicles for automotive, military, and aerospace applications. The high computational demand of state-of-the-art CNN architectures requires the use of hardware acceler...

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Veröffentlicht in:	IEEE transactions on nuclear science Jg. 68; H. 5; S. 865 - 872
Hauptverfasser:	Libano, F., Rech, P., Neuman, B., Leavitt, J., Wirthlin, M., Brunhaver, J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Autonomous cars Biological neural networks Computer applications Data reduction Failure rates Field programmable gate arrays Field-programmable gate array (FPGA) Floating point arithmetic Image processing Military applications Network reliability Neural networks Neutron beams Parallel processing parallelism Power consumption Quantization (signal) Radiation Radiation effects reduced precision Reliability Reliability analysis Resource management Sensitivity
ISSN:	0018-9499, 1558-1578
Online-Zugang:	Volltext
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