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 widearea network transmission, either Endonly mode or Cloudonly 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 stateoftheart methods.

Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection Deep neural networks ( DNNs ) are becoming a key enabling technology for many application domains. However, ondevice inference on batterypowered, resourceconstrained embedding systems is often infeasible due to prohibitively long inferencing time and resource requirements of many DNNs. Offloading computation into the cloud is often unacceptable due to privacy concerns, high latency, or the lack of connectivity. While compression algorithms often succeed in reducing inferencing times, they come at the cost of reduced accuracy. This paper presents a new, alternative approach to enable efficient execution of DNNs on embedded devices. Our approach dynamically determines which DNN to use for a given input, by considering the desired accuracy and inference time. It employs machine learning to develop a lowcost predictive model to quickly select a pretrained DNN to use for a given input and the optimization constraint. We achieve this by first offline training a predictive model, and then using the learned model to select a DNN model to use for new, unseen inputs. We apply our approach to two representative DNN domains: image classification and machine translation. We evaluate our approach on a Jetson TX2 embedded deep learning platform and consider a range of influential DNN models including convolutional and recurrent neural networks. For image classification, we achieve a 1.8x reduction in inference time with a 7.52% improvement in accuracy, over the mostcapable single DNN model. For machine translation, we achieve a 1.34x reduction in inference time over the mostcapable single model, with little impact on the quality of translation.