Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As sensor-equipped Internet of Things (IoT) devices permeate into every aspect of modern life, the ability to execute CNN inference, a computationally intensive application, on resource constrained devices has become increasingly important. In this context, we present Cappuccino, a framework for synthesis of efficient inference software targeting mobile System-on-Chips (SoCs). We propose techniques for efficient parallelization of CNN inference targeting mobile SoCs, and explore the underlying tradeoffs. Experiments with different CNNs on three mobile devices demonstrate the effectiveness of our approach.
Table compares the performance of software synthesized by Cappuccino with the state-of-the-art work @cite_7 . The proposed solution under exact arithmetic improves the execution time by 1.38X. In addition, when the synthesized software is both parallel and imprecise, it shows up to 11.47X speedup compared to CNNDroid @cite_7 .
@cite_7
2525951180
Many mobile applications running on smartphones and wearable devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However, performance and energy consumption limitations make the execution of such computationally intensive algorithms on mobile devices prohibitive. We present a GPU-accelerated library, dubbed CNNdroid [1], for execution of trained deep CNNs on Android-based mobile devices. Empirical evaluations show that CNNdroid achieves up to 60X speedup and 130X energy saving on current mobile devices. The CNNdroid open source library is available for download at https: github.com ENCP CNNdroid