Mobile edge computing is conducive to artificial intelligence computing near terminals, in which Deep Neural Networks (DNNs) should be partitioned to allocate tasks partially to the edge for execution to reduce latency and save energy. Most of the existing studies assume that the tasks are of the same type or the computing resources of the server are the same. In real life, Mobile Devices (MDs) and Edge Servers (ESs) are heterogeneous in type and computing resources, it is challenging to find the optimal partition point for each DNN and offload it to an appropriate ES. To fill this gap, we pro...
