Poster: Scaling Up Deep Neural Network optimization for Edge Inference†

摘要

Deep neural networks (DNNs) have been increasingly deployed on and integrated with edge devices, such as mobile phones, drones, robots and wearables. Compared to cloud-based inference, running DNN inference directly on edge devices (a.k. a. edge inference) has major advantages, including being free from the network connection requirement, saving bandwidths, and better protecting user privacy [1].