Abstract
As the development of lightweight deep learning algorithms, various deep neural network (DNN) models have been proposed for the remote sensing scene classification (RSSC) application. However, it is still challenging for these RSSC models to achieve optimal performance among model accuracy, inference latency, and energy consumption on resource-constrained edge devices. In this paper, we propose a lightweight RSSC framework, which includes a distilled global filter network (GFNet) model and an early-exit mechanism designed for edge devices to achieve state-of-the-art performance. Specifically, we first apply frequency domain distillation on the GFNet model to reduce model size. Then we design a dynamic early-exit model tailored for DNN models on edge devices to further improve model inference efficiency. We evaluate our E3C model on three edge devices across four datasets. Extensive experimental results show that it achieves an average of 1.3x speedup on model inference and over 40% improvement on energy efficiency, while maintaining high classification accuracy.
Abstract (translated)
随着轻量级深度学习算法的发展，各种用于遥感场景分类（RSSC）应用的深层神经网络（DNN）模型被提出。然而，在资源受限的边缘设备上，这些RSSC模型在模型准确度、推断延迟和能耗之间达到最优性能仍然具有挑战性。本文中，我们提出了一种轻量级的RSSC框架，该框架包括一个蒸馏全局滤波网络（GFNet）模型以及一种为边缘设备设计的提前退出机制，以实现最先进的性能。具体来说，我们首先在GFNet模型上应用频域知识蒸馏来减小模型大小；然后我们设计了一种动态提前退出模型，专门针对边缘设备上的DNN模型，进一步提高模型推断效率。 我们在三个不同边缘设备和四个数据集上评估了我们的E3C模型。大量的实验结果表明，它在模型推断速度方面平均提高了1.3倍，并且能耗效率提高了40%以上，同时保持了较高的分类准确性。
URL
https://arxiv.org/abs/2507.20623
