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Machine learning has changed the computing paradigm. Products today are builtwith machine intelligence as a central attribute, and consumers are beginningto expect near-human interaction with the appliances they use. However, much ofthe deep learning revolution has been limited to the cloud. Recently, severalmachine learning packages based on edge devices have been announced which aimto offload the computing to the edges. However, little research has been doneto evaluate these packages on the edges, making it difficult for end users toselect an appropriate pair of software and hardware. In this paper, we make aperformance comparison of several state-of-the-art machine learning packages onthe edges, including TensorFlow, Caffe2, MXNet, PyTorch, and TensorFlow Lite.We focus on evaluating the latency, memory footprint, and energy of these toolswith two popular types of neural networks on different edge devices. Thisevaluation not only provides a reference to select appropriate combinations ofhardware and software packages for end users but also points out possiblefuture directions to optimize packages for developers.

pCAMP: Performance Comparison of Machine Learning Packages on the Edges