In this paper, we propose a generic Runtime Network Routing (RNR) framework for efficient image classification, which selects an optimal path inside the network. Unlike existing static neural network acceleration methods, our method preserves the full ability of the original large network and conducts dynamic routing at runtime according to the input image and current feature maps. The routing is performed in a bottom-up, layer-by-layer manner, where we model it as a Markov decision process and use reinforcement learning for training. The agent determines the estimated reward of each sub-path and conducts routing conditioned on different samples, where a faster path is taken when the image is easier for the task. Since the ability of network is fully preserved, the balance point is easily adjustable according to the available resources. We test our method on both multi-path residual networks and incremental convolutional channel pruning, and show that RNR consistently outperforms static methods at the same computation complexity on both the CIFAR and ImageNet datasets. Our method can also be applied to off-the-shelf neural network structures and easily extended to other application scenarios.

中文翻译：

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运行时网络路由以实现有效的图像分类

在本文中，我们提出了用于有效图像分类的通用运行时网络路由（RNR）框架，该框架选择了网络内部的最佳路径。与现有的静态神经网络加速方法不同，我们的方法保留了原始大型网络的全部功能，并根据输入图像和当前特征图在运行时进行动态路由。路由以自下而上，逐层的方式执行，我们将其建模为马尔可夫决策过程，并使用强化学习进行训练。代理确定每个子路径的估计报酬，并根据不同的样本进行路由，其中​​当图像更易于执行任务时，将采用较快的路径。由于网络的能力得到了充分保留，因此可以根据可用资源轻松调整平衡点。我们在多径残差网络和增量卷积信道修剪上测试了我们的方法，并显示在CIFAR和ImageNet数据集上，在相同的计算复杂度下，RNR始终优于静态方法。我们的方法还可以应用于现成的神经网络结构，并可以轻松地扩展到其他应用场景。