论文标题
动态节能的神经网络（TNN）
Dynamically Throttleable Neural Networks (TNN)
论文作者
论文摘要
深度神经网络（DNN）的条件计算通过运行网络的子集来降低总体计算负载，并提高模型的准确性。在这项工作中，我们提出了一个可运行的可节奏神经网络（TNN），可以自适应地自我调节自己的性能目标和计算资源。我们设计了具有多种属性的TNN，可以基于运行时上下文实现动态执行的灵活性。 TNN定义为使用单独训练的控制器控件的节流模块，该模块会生成单个利用控制参数。我们验证了许多实验的建议，包括使用CIFAR-10和Imagenet数据集，包括卷积神经网络（CNN，例如VGG，Resnet，Resnext，densenet），用于对象分类和识别任务。我们还证明了动态TNN执行在3D Convolustion网络（C3D）上的有效性，以完成手势任务。结果表明，与香草解决方案相比，TNN可以保持峰准确性的性能，同时可以优雅地减少计算需求，延迟降低至74％，节省52％。
Conditional computation for Deep Neural Networks (DNNs) reduce overall computational load and improve model accuracy by running a subset of the network. In this work, we present a runtime throttleable neural network (TNN) that can adaptively self-regulate its own performance target and computing resources. We designed TNN with several properties that enable more flexibility for dynamic execution based on runtime context. TNNs are defined as throttleable modules gated with a separately trained controller that generates a single utilization control parameter. We validate our proposal on a number of experiments, including Convolution Neural Networks (CNNs such as VGG, ResNet, ResNeXt, DenseNet) using CiFAR-10 and ImageNet dataset, for object classification and recognition tasks. We also demonstrate the effectiveness of dynamic TNN execution on a 3D Convolustion Network (C3D) for a hand gesture task. Results show that TNN can maintain peak accuracy performance compared to vanilla solutions, while providing a graceful reduction in computational requirement, down to 74% reduction in latency and 52% energy savings.
