论文标题
一种用于减少DNN上瓷砖修剪损失的一次性重新聚集方法
A One-Shot Reparameterization Method for Reducing the Loss of Tile Pruning on DNNs
论文作者
论文摘要
最近，对瓷砖修剪进行了广泛的研究，以加速深度神经网络（DNNS）的推断。但是，我们发现，由于训练有素的DNN，由于瓷砖修剪而造成的损失可以消除重要元素，并消除重要的元素。在这项研究中，我们提出了一种称为tiletrans的单发重新聚集方法，以减少瓷砖修剪的损失。具体而言，我们重复了权重矩阵的行或列，以使模型体系结构在重新聚体化后可以保持不变。这种再生能力实现了DNN模型的重新聚集，而无需进行任何重新培训。提出的重新聚集方法将重要元素结合到同一瓷砖中。因此，在修剪瓷砖修剪后保留重要要素。此外，可以将tiletrans无缝集成到现有的瓷砖修剪方法中，因为它是一种在修剪之前执行的预处理方法，这与大多数现有方法是正交的。实验结果表明，我们的方法对于减少DNN上瓷砖修剪的损失至关重要。具体而言，Alexnet的精度提高了高达17％，而Resnet-34的精度为5％，其中两种模型均在Imagenet上进行了预训练。
Recently, tile pruning has been widely studied to accelerate the inference of deep neural networks (DNNs). However, we found that the loss due to tile pruning, which can eliminate important elements together with unimportant elements, is large on trained DNNs. In this study, we propose a one-shot reparameterization method, called TileTrans, to reduce the loss of tile pruning. Specifically, we repermute the rows or columns of the weight matrix such that the model architecture can be kept unchanged after reparameterization. This repermutation realizes the reparameterization of the DNN model without any retraining. The proposed reparameterization method combines important elements into the same tile; thus, preserving the important elements after the tile pruning. Furthermore, TileTrans can be seamlessly integrated into existing tile pruning methods because it is a pre-processing method executed before pruning, which is orthogonal to most existing methods. The experimental results demonstrate that our method is essential in reducing the loss of tile pruning on DNNs. Specifically, the accuracy is improved by up to 17% for AlexNet while 5% for ResNet-34, where both models are pre-trained on ImageNet.
