TIDE: Task-Driven DNN Training and Splitting for Efficient Inference at the Mobile Edge

The growing demands of DNN-based inference at the mobile edge is driving the need for increasingly efficient execution. Such applications often require fast and high-quality outputs, which are hard to realize due to the limited computa- tional and communication capabilities at the edge. This paper tackles these issues focusing on a DNN for the execution of tasks that are homogeneous in nature but heterogeneous in their domains. The key idea is to start with a parent DNN of interconnected computational elements (atoms), and strategically form a collection of task-specific DNNs suitable for distributed deployment. Such task-specific DNNs may include common as well as uniquely used atoms of the parent DNN. Ultimately, the aim is that they be smaller in size – thus a better match for edge resources – and achieve low-cost inference. We solve the problem of determining the best collection of task-specific DNNs through an algorithmic framework named TIDE. Experimental results show that TIDE decreases inference cost and time by 90% and 80% (resp.) relatively to centralized approaches, and by over 60% and 70% (resp.) when compared to the best benchmark.