论文标题
利用卷积神经网络的结构修剪
Leveraging Structured Pruning of Convolutional Neural Networks
论文作者
论文摘要
结构化修剪是一种降低卷积神经网络成本的流行方法，这是许多计算机视觉任务中最先进的方法。但是，根据体系结构的不同，修剪引入了维数差异，以防止实际减少修剪的网络。为了解决这个问题，我们提出了一种能够采用任何结构化的修剪面罩并产生一个不会遇到这些问题的网络并可以有效利用的网络。我们提供了对解决方案的准确描述，并在嵌入式硬件，修剪的卷积神经网络上显示了收益的结果，能源消耗和推理时间。
Structured pruning is a popular method to reduce the cost of convolutional neural networks, that are the state of the art in many computer vision tasks. However, depending on the architecture, pruning introduces dimensional discrepancies which prevent the actual reduction of pruned networks. To tackle this problem, we propose a method that is able to take any structured pruning mask and generate a network that does not encounter any of these problems and can be leveraged efficiently. We provide an accurate description of our solution and show results of gains, in energy consumption and inference time on embedded hardware, of pruned convolutional neural networks.
