Lance: efficient low-precision quantized winograd convolution for neural networks based on graphics processing units

Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry. In this paper, we propose an efficient low-precision quantized Winograd convolution algorithm, called LANCE, which combines the advantages of fast convolution and quantization...

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Veröffentlicht in:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 3842 - 3846
Hauptverfasser:	Li, Guangli, Liu, Lei, Wang, Xueying, Ma, Xiu, Feng, Xiaobing
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.05.2020
Schlagworte:	Convolution deep learning Graphics processing units Industries linear quantization low-precision computing Neural networks Quantization (signal) Signal processing algorithms Speech processing Winograd convolution
ISSN:	2379-190X
Online-Zugang:	Volltext
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