论文标题
CNN通过低级近似与量化因子加速
CNN Acceleration by Low-rank Approximation with Quantized Factors
论文作者
论文摘要
现代的卷积神经网络尽管在解决复杂的计算机视觉任务任务方面取得了出色的成果，但由于对计算复杂性，内存和功耗的严格要求，仍无法在移动和嵌入式设备中有效使用。 CNN必须在部署前压缩和加速。为了解决这个问题，提出了新的方法结合了两种已知方法，提出了塔克格式的低级张量近似以及权重和特征图的量化（激活）。提出了用于多线性等级选择任务的贪婪的一步和多步算法。开发了应用塔克分解和量化后的质量恢复方法。 CIFAR-10，CIFAR-100和Imagenet分类任务上的RESNET18和RESNET34证明了我们方法的效率。由于对其他方法进行了比较分析，用于压缩和加速，我们的方法显示了其有希望的特征。
The modern convolutional neural networks although achieve great results in solving complex computer vision tasks still cannot be effectively used in mobile and embedded devices due to the strict requirements for computational complexity, memory and power consumption. The CNNs have to be compressed and accelerated before deployment. In order to solve this problem the novel approach combining two known methods, low-rank tensor approximation in Tucker format and quantization of weights and feature maps (activations), is proposed. The greedy one-step and multi-step algorithms for the task of multilinear rank selection are proposed. The approach for quality restoration after applying Tucker decomposition and quantization is developed. The efficiency of our method is demonstrated for ResNet18 and ResNet34 on CIFAR-10, CIFAR-100 and Imagenet classification tasks. As a result of comparative analysis performed for other methods for compression and acceleration our approach showed its promising features.
