IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantly, variable. Furthermore, in cyber-physical systems interacting with the physical environment, image offloading is also commonly subject to timing constraints. It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices. In this paper, we use image classification as our target application and propose progressive neural compression (PNC) as an efficient solution to this problem. Although neural compression has been used to compress images for different ML applications, existing solutions often produce fixed-size outputs that are unsuitable for timing-constrained offloading over variable bandwidth. To address this limitation, we train a multi-objective rateless autoencoder that optimizes for multiple compression rates via stochastic taildrop to create a compression solution that produces features ordered according to their importance to inference performance. Features are then transmitted in that order based on available bandwidth, with classification ultimately performed using the (sub)set of features received by the deadline. We demonstrate the benefits of PNC over state-of-the-art neural compression approaches and traditional compression methods on a testbed comprising an IoT device and an edge server connected over a wireless network with varying bandwidth.

翻译：物联网设备日益成为边缘服务器上机器学习应用的数据源。设备到服务器的数据传输通常经由本地无线网络，其带宽不仅有限，更关键的是具有时变性。此外，在与物理环境交互的信息物理系统中，图像卸载通常还受时序约束。因此，亟需开发一种自适应方法，在满足时序约束和物联网设备资源约束的前提下，最大化机器学习应用的推理性能。本文以图像分类为目标应用，提出采用渐进式神经压缩（PNC）作为解决该问题的有效方案。尽管神经压缩已被用于压缩不同机器学习应用的图像，但现有方案通常生成固定尺寸输出，难以适用于可变带宽下的时序约束卸载场景。为解决此限制，我们训练了一个多目标无速率自编码器，通过随机尾部丢弃优化多压缩率，构建产生按推理重要性排序特征的压缩方案。特征根据可用带宽按序传输，最终基于截止时间前收到的（子）特征集完成分类。我们在包含物联网设备与边缘服务器（通过可变带宽无线网络连接）的测试平台上，验证了PNC相较于当前最优神经压缩方法与传统压缩方法的优越性。