  Implementing machine learning algorithms on Internet of things (IoT) devices
has become essential for emerging applications, such as autonomous driving,
environment monitoring. But the limitations of computation capability and
energy consumption make it difficult to run complex machine learning algorithms
on IoT devices, especially when latency deadline exists. One solution is to
offload the computation intensive tasks to the edge server. However, the
wireless uploading of the raw data is time consuming and may lead to deadline
violation. To reduce the communication cost, lossy data compression can be
exploited for inference tasks, but may bring more erroneous inference results.
In this paper, we propose a dynamic compression ratio selection scheme for edge
inference system with hard deadlines. The key idea is to balance the tradeoff
between communication cost and inference accuracy. By dynamically selecting the
optimal compression ratio with the remaining deadline budgets for queued tasks,
more tasks can be timely completed with correct inference under limited
communication resources. Furthermore, information augmentation that retransmits
less compressed data of task with erroneous inference, is proposed to enhance
the accuracy performance. While it is often hard to know the correctness of
inference, we use uncertainty to estimate the confidence of the inference, and
based on that, jointly optimize the information augmentation and compression
ratio selection. Lastly, considering the wireless transmission errors, we
further design a retransmission scheme to reduce performance degradation due to
packet losses. Simulation results show the performance of the proposed schemes
under different deadlines and task arrival rates.
