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Convolutional Neural Networks (CNNs) exhibit remarkable performance invarious machine learning tasks. As sensor-equipped Internet of Things (IoT)devices permeate into every aspect of modern life, the ability to execute CNNinference, a computationally intensive application, on resource constraineddevices has become increasingly important. In this context, we presentCappuccino, a framework for synthesis of efficient inference software targetingmobile System-on-Chips (SoCs). We propose techniques for efficientparallelization of CNN inference targeting mobile SoCs, and explore theunderlying tradeoffs. Experiments with different CNNs on three mobile devicesdemonstrate the effectiveness of our approach.

Cappuccino: Efficient Inference Software Synthesis for Mobile System-on-Chips