Deep learning solutions are being increasingly used in mobile applications. Although\nthere are many open-source software tools for the development of deep learning solutions, there are\nno guidelines in one place in a unified manner for using these tools toward real-time deployment\nof these solutions on smartphones. From the variety of available deep learning tools, the most\nsuited ones are used in this paper to enable real-time deployment of deep learning inference\nnetworks on smartphones. A uniform flow of implementation is devised for both Android and iOS\nsmartphones. The advantage of using multi-threading to achieve or improve real-time throughputs\nis also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption,\nand real-time throughput is considered for validation purposes. The developed deployment approach\nallows deep learning models to be turned into real-time smartphone apps with ease based on publicly\navailable deep learning and smartphone software tools. This approach is applied to six popular\nor representative convolutional neural network models, and the validation results based on the\nbenchmarking metrics are reported.
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