  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.
