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On-device inference of machine learning models for mobile phones is desirabledue to its lower latency and increased privacy. Running such acompute-intensive task solely on the mobile CPU, however, can be difficult dueto limited computing power, thermal constraints, and energy consumption. Appdevelopers and researchers have begun exploiting hardware accelerators toovercome these challenges. Recently, device manufacturers are adding neuralprocessing units into high-end phones for on-device inference, but theseaccount for only a small fraction of hand-held devices. In this paper, wepresent how we leverage the mobile GPU, a ubiquitous hardware accelerator onvirtually every phone, to run inference of deep neural networks in real-timefor both Android and iOS devices. By describing our architecture, we alsodiscuss how to design networks that are mobile GPU-friendly. Ourstate-of-the-art mobile GPU inference engine is integrated into the open-sourceproject TensorFlow Lite and publicly available at https://tensorflow.org/lite.

On-Device Neural Net Inference with Mobile GPUs