  Device-edge collaboration on deep neural network (DNN) inference is a
promising approach to efficiently utilizing network resources for supporting
artificial intelligence of things (AIoT) applications. In this paper, we
propose a novel digital twin (DT)-assisted approach to device-edge
collaboration on DNN inference that determines whether and when to stop local
inference at a device and upload the intermediate results to complete the
inference on an edge server. Instead of determining the collaboration for each
DNN inference task only upon its generation, multi-step decision-making is
performed during the on-device inference to adapt to the dynamic computing
workload status at the device and the edge server. To enhance the adaptivity, a
DT is constructed to evaluate all potential offloading decisions for each DNN
inference task, which provides augmented training data for a machine
learning-assisted decision-making algorithm. Then, another DT is constructed to
estimate the inference status at the device to avoid frequently fetching the
status information from the device, thus reducing the signaling overhead. We
also derive necessary conditions for optimal offloading decisions to reduce the
offloading decision space. Simulation results demon-strate the outstanding
performance of our DT-assisted approach in terms of balancing the tradeoff
among inference accuracy, delay, and energy consumption.
