iBranchy: An Accelerated Edge Inference Platform for loT Devices◊

Abstract

With the phenomenal growth of IoT devices at the network edge, many new applications have emerged, including remote health monitoring, augmented reality, and video analytics. However, se-curing these devices from different network attacks has remained a major challenge. To enable more secure services for IoT devices, threats must be discovered quickly in the network edge and effi-ciently dealt with within device resource constraints. Deep Neural Networks (DNN) have emerged as solution to provide both security and high performance. However, existing edge-based IoT DNN clas-sifiers are neither lightweight nor flexible to perform conditional computation based on device types to save edge resources. Dynamic deep neural networks have recently emerged as a technique that can accelerate inference by performing conditional computation and, therefore, save computational resources. In this work, we de-sign and develop an accelerated IoT classifier iBranchy based on a dynamic neural network that can perform quick inference with fewer edge resources while also providing flexibility to adapt to different hardware and network conditions. CCS CONCEPTS • Security and privacy → Mobile and wireless security; • Com-puting methodologies → Neural networks.