Start Time: 10:00 am
End Time: 10:30 am
In this presentation, we explore practical aspects of implementing a pre-trained deep neural network (DNN) inference on typical edge processors. First, we briefly touch on how we evaluate the accuracy of DNNs for use in real-world applications. Next, we explain the process for converting a trained model in TensorFlow into formats suitable for deployment at the edge and examine a simple, generic C++ real-time inference application that can be deployed on a variety of hardware platforms. We then outline a method for evaluating the performance of edge DNN implementations and show the results of utilizing this method to benchmark the performance of three popular edge computing platforms: The Google Coral (based on the Edge TPU), NVIDIA Jetson Nano and Raspberry Pi 3.