The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this article, we present a novel fusion-catalyzed pruning approach, called FuPruner, which simultaneously optimizes the parametric and nonparametric operators for accelerating neural networks. We introduce an aggressive fusion method to equivalently transform a model, which extends the optimization space of pruning and enables nonparametric operators to be pruned in a similar manner as parametric operators, and a dynamic filter pruning method is applied to decrease the computational cost of models while retaining the accuracy requirement. Moreover, FuPruner provides configurable optimization options for controlling fusion and pruning, allowing much more flexible performance-accuracy tradeoffs to be made. Evaluation with state-of-the-art residual neural networks on five representative intelligent edge platforms, Jetson TX2, Jetson Nano, Edge tensor processing unit, neural compute stick, and neural compute stick 2, demonstrates the effectiveness of our approach, which can accelerate the inference of models on CIFAR-10 and ImageNet datasets.

中文翻译：

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用于优化智能边缘设备上深度学习的融合催化剪枝


深度神经网络模型不断增加的计算成本限制了智能应用程序在资源受限的边缘设备上的适用性。虽然已经提出了许多神经网络修剪方法来压缩模型，但流行的方法仅关注参数算子（例如，卷积），这可能会错过优化机会。在本文中，我们提出了一种新颖的融合催化剪枝方法，称为 FuPruner，它同时优化参数和非参数算子以加速神经网络。我们引入了一种积极的融合方法来等效地变换模型，它扩展了剪枝的优化空间，使得非参数算子能够以与参数算子类似的方式剪枝，并应用动态滤波器剪枝方法来降低模型的计算成本，同时保留精度要求。此外，FuPruner 提供了用于控制融合和修剪的可配置优化选项，允许进行更灵活的性能与准确性权衡。在五个代表性智能边缘平台（Jetson TX2、Jetson Nano、边缘张量处理单元、神经计算棒和神经计算棒 2）上使用最先进的残差神经网络进行评估，证明了我们方法的有效性，可以加速CIFAR-10 和 ImageNet 数据集上的模型推理。