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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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 3842 - 3846
Main Authors: Li, Guangli, Liu, Lei, Wang, Xueying, Ma, Xiu, Feng, Xiaobing
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2020
Subjects:
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 Access:Get full text
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Summary: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 techniques. By embedding linear quantization operations into the Winograd-domain, the fast convolution can be performed efficiently under low-precision computation on graphics processing units. We test neural network models with LANCE on representative image classification datasets, including SVHN, CIFAR, and ImageNet. The experimental results show that our 8-bit quantized Winograd convolution improves the performance by up to 2.40× over the full-precision convolution with trivial accuracy loss.
ISSN:2379-190X
DOI:10.1109/ICASSP40776.2020.9054562