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.

翻译：结构化修剪是一种降低进化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技术,然而,根据建筑结构,修剪工程引入了阻止净化网络实际减少的尺寸差异。为了解决这一问题,我们建议了一种方法,可以使用任何结构化修剪面罩,形成一个没有遇到任何这些问题并能有效利用的网络。我们准确地描述了我们的解决方案,并显示了内置硬件的能量消耗和推导时间、精密的进化神经网络的增益结果。