论文标题
嵌入式GPU上修剪的语义分割网络的能耗分析
Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU
论文作者
论文摘要
在许多计算机视觉任务中，深度神经网络是最新的。它们在自动驾驶汽车的背景下的部署特别令人感兴趣，因为它们在能源消耗方面的局限性禁止使用非常大的网络，这通常达到最佳性能。在不牺牲准确性的情况下，降低这些体系结构复杂性的一种常见方法是依靠修剪，其中消除了最不重要的部分。关于该主题的文献很大，但有趣的是，很少有作品衡量修剪对能量的实际影响。在这项工作中，我们有兴趣使用CityScapes数据集在语义细分的特定语义细分中对其进行测量。为此，我们分析了最近提出的结构化修剪方法的影响，当训练有素的体系结构被部署在Jetson Xavier嵌入的GPU上。
Deep neural networks are the state of the art in many computer vision tasks. Their deployment in the context of autonomous vehicles is of particular interest, since their limitations in terms of energy consumption prohibit the use of very large networks, that typically reach the best performance. A common method to reduce the complexity of these architectures, without sacrificing accuracy, is to rely on pruning, in which the least important portions are eliminated. There is a large literature on the subject, but interestingly few works have measured the actual impact of pruning on energy. In this work, we are interested in measuring it in the specific context of semantic segmentation for autonomous driving, using the Cityscapes dataset. To this end, we analyze the impact of recently proposed structured pruning methods when trained architectures are deployed on a Jetson Xavier embedded GPU.
