论文标题
DNNS的量化Winograd/Toom-Cook卷积：超越规范的多项式基础
Quantaized Winograd/Toom-Cook Convolution for DNNs: Beyond Canonical Polynomials Base
论文作者
论文摘要
近年来，研究了如何加快深层神经网络中卷积计算的问题。 Winograd卷积算法是一种大大降低时间消耗的常用方法。但是，它遇到了数值准确性的问题，特别是对于较低的精确度。在本文中，我们介绍了基本变更技术在量化Winograd感知培训模型中的应用。我们表明，对于测试网络（RESNET18）和数据集（CIFAR10），我们可以将$ 8 $位定量的网络训练至几乎相同的精度（最高0.5％），并且对于量化了直接卷积，并且在前/邮政变换中几乎没有其他操作。将Hadamard产品保持在$ 9 $位的位置使我们获得与直接卷积相同的精度。
The problem how to speed up the convolution computations in Deep Neural Networks is widely investigated in recent years. The Winograd convolution algorithm is a common used method that significantly reduces time consumption. However, it suffers from a problem with numerical accuracy particularly for lower precisions. In this paper we present the application of base change technique for quantized Winograd-aware training model. We show that we can train the $8$ bit quantized network to nearly the same accuracy (up to 0.5% loss) for tested network (Resnet18) and dataset (CIFAR10) as for quantized direct convolution with few additional operations in pre/post transformations. Keeping Hadamard product on $9$ bits allow us to obtain the same accuracy as for direct convolution.
