ADDA: Adaptive Distributed DNN Inference Acceleration in Edge Computing Environment

Implementing intelligent mobile applications on IoT devices with DNN technology has become an inevitable trend. Due to the limitations of the size of DNN model deployed onto end devices and the instability of wide-area network transmission, either End-only mode or Cloud-only mode cannot guarantee the reasonable latency and recognition accuracy simultaneously. A better solution is to exploit the edge computing, where the existing edge computing execution framework and offloading mechanism for DNN inference suffer unnecessary computational overheads and underutilized computing capacity of end and edge. To address these shortcomings, an adaptive distributed DNN inference acceleration framework for edge computing environment is proposed in this paper, where DNN computation path optimization and DNN computation partition optimization are taken into consideration. The evaluations demonstrate that our method can effectively accelerate the DNN inference compared to the state-of-the-art methods.