Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices

The great success of deep neural networks (DNNs) has significantly assisted humans in numerous applications such as computer vision. DNNs are widely used in today's applications and systems. However, in-the-edge inference of DNNs is still a severe challenge mainly because of the contradiction between the inherent intensive resource requirements of DNNs and the tight resource availability of edge devices. Nevertheless, in-the-edge inferencing preserves privacy in several user-centric domains and applies in several scenarios with limited Internet connectivity (e.g., drones, robots, autonomous vehicles). That is why several companies have released specialized edge devices for accelerating the execution performance of DNNs in the edge. Although preliminary studies have characterized such edge devices separately, a unified comparison with the same set of assumptions has not been fully performed. In this paper, we endeavor to address this knowledge gap by characterizing several commercial edge devices on popular frameworks using well-known convolution neural networks (CNNs), a type of DNN. We analyze the impact of frameworks, their software stack, and their implemented optimizations on the final performance. Moreover, we measure energy consumption and temperature behavior of these edge devices.