论文标题
与修剪的无线链接的联合设备边缘推断
Joint Device-Edge Inference over Wireless Links with Pruning
论文作者
论文摘要
我们提出了一个联合特征压缩和传输方案，以在无线网络边缘有效推理。我们的目标是在边缘设备处有限的计算资源，在边缘服务器上启用高效且可靠的推断。先前的工作主要集中在特征压缩上，忽略了通道编码的计算成本。我们结合了最近提出的深关节源通道编码（DEEPJSCC）方案，并将其与旨在降低神经网络冗余复杂性的新型过滤器修剪策略相结合。我们在分类任务上评估了我们的方法，并在边缘设备的端到端可靠性和降低工作负载方面显示出改进的结果。这是将DEEPJSCC与网络修剪结合在一起的第一项工作，并将其应用于无线边缘的图像分类。
We propose a joint feature compression and transmission scheme for efficient inference at the wireless network edge. Our goal is to enable efficient and reliable inference at the edge server assuming limited computational resources at the edge device. Previous work focused mainly on feature compression, ignoring the computational cost of channel coding. We incorporate the recently proposed deep joint source-channel coding (DeepJSCC) scheme, and combine it with novel filter pruning strategies aimed at reducing the redundant complexity from neural networks. We evaluate our approach on a classification task, and show improved results in both end-to-end reliability and workload reduction at the edge device. This is the first work that combines DeepJSCC with network pruning, and applies it to image classification over the wireless edge.
