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Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
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Mach. Learn. Knowl. Extr. 2019, 1(1), 450-465; https://doi.org/10.3390/make1010027
Received: 9 January 2019 / Revised: 4 February 2019 / Accepted: 6 February 2019 / Published: 13 February 2019
(This article belongs to the Section Learning)
Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported. View Full-Text
Keywords: deployment of deep learning models on smartphones; real-time smartphone apps of deep learning models; benchmarking deep learning apps on smartphones deployment of deep learning models on smartphones; real-time smartphone apps of deep learning models; benchmarking deep learning apps on smartphones
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Sehgal, A.; Kehtarnavaz, N. Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps. Mach. Learn. Knowl. Extr. 2019, 1, 450-465.

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