Convolutional Neural Networks (CNNs) have been shown to achieve state of the art results on several image processing tasks such as classification, localization, and segmentation. Convolutional and fully connected layers form the building blocks of these networks. The convolution layers are responsible for the majority of the computations even though they have fewer parameters. As inference is used much more than training (which happens only once), it is important to reduce the computations of the network for this phase. This work presents a systematic procedure to trim CNNs by identifying the least important features in the convolution layers and replacing them either with approximations or kernels of reduced size. We also propose an algorithm to integrate the lower kernel approximation technique for a given accuracy budget. We show that using the linear approximation method can achieve a 15% – 80% savings with a median of 52% reduction while the lower kernel method can achieve 33% – 95% reduction with a median of 65% in the required number of computations with only a marginal 1% loss in accuracy across several benchmark datasets. We have also demonstrated the proposed methods on VGG-16 architecture for various datasets. On VGG-16 we have achieved 4.2% - 45% savings in MAC computations (with a median of 18.5%) with only a marginal 0.5% loss in accuracy. We also show how an existing hardware accelerator for DNNs (DianNao) can be modified with low added complexity to take advantage of the kernel approximations, and estimate the speedups that can be obtained in such a way on custom embedded hardware.

中文翻译：

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通过特征近似和内核尺寸减小来实现更纤薄的 CNN

卷积神经网络 (CNN) 已被证明可以在分类、定位和分割等多种图像处理任务上实现最先进的结果。卷积层和全连接层构成了这些网络的构建块。卷积层负责大部分计算，尽管它们的参数较少。由于推理的使用远多于训练（仅发生一次），因此减少此阶段的网络计算量非常重要。这项工作提出了一种通过识别卷积层中最不重要的特征并用近似值或减小尺寸的内核替换它们来修剪 CNN 的系统过程。我们还提出了一种算法，用于针对给定的精度预算集成较低的内核近似技术。我们表明，使用线性近似方法可以节省 15% – 80%，中位数减少 52%，而下核方法可以实现 33% – 95% 的减少，中位数减少 65% 所需的计算量多个基准数据集的准确率仅损失 1%。我们还针对各种数据集在 VGG-16 架构上演示了所提出的方法。在 VGG-16 上，我们的 MAC 计算节省了 4.2% - 45%（中位数为 18.5%），而准确率仅损失了 0.5%。我们还展示了如何以较低的复杂性修改现有的 DNN 硬件加速器 (DianNao)，以利用内核近似，并估计在定制嵌入式硬件上以这种方式可以获得的加速。