  The main challenge to deploy deep neural network (DNN) over a mobile edge
network is how to split the DNN model so as to match the network architecture
as well as all the nodes' computation and communication capacity. This
essentially involves two highly coupled procedures: model generating and model
splitting. In this paper, a joint model split and neural architecture search
(JMSNAS) framework is proposed to automatically generate and deploy a DNN model
over a mobile edge network. Considering both the computing and communication
resource constraints, a computational graph search problem is formulated to
find the multi-split points of the DNN model, and then the model is trained to
meet some accuracy requirements. Moreover, the trade-off between model accuracy
and completion latency is achieved through the proper design of the objective
function. The experiment results confirm the superiority of the proposed
framework over the state-of-the-art split machine learning design methods.
