  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 2.40x over the full-precision
convolution with trivial accuracy loss.
