  Device-edge co-inference opens up new possibilities for resource-constrained
wireless devices (WDs) to execute deep neural network (DNN)-based applications
with heavy computation workloads. In particular, the WD executes the first few
layers of the DNN and sends the intermediate features to the edge server that
processes the remaining layers of the DNN. By adapting the model splitting
decision, there exists a tradeoff between local computation cost and
communication overhead. In practice, the DNN model is re-trained and updated
periodically at the edge server. Once the DNN parameters are regenerated, part
of the updated model must be placed at the WD to facilitate on-device
inference. In this paper, we study the joint optimization of the model
placement and online model splitting decisions to minimize the energy-and-time
cost of device-edge co-inference in presence of wireless channel fading. The
problem is challenging because the model placement and model splitting
decisions are strongly coupled, while involving two different time scales. We
first tackle online model splitting by formulating an optimal stopping problem,
where the finite horizon of the problem is determined by the model placement
decision. In addition to deriving the optimal model splitting rule based on
backward induction, we further investigate a simple one-stage look-ahead rule,
for which we are able to obtain analytical expressions of the model splitting
decision. The analysis is useful for us to efficiently optimize the model
placement decision in a larger time scale. In particular, we obtain a
closed-form model placement solution for the fully-connected multilayer
perceptron with equal neurons. Simulation results validate the superior
performance of the joint optimal model placement and splitting with various DNN
structures.
