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The PAAMS Collection, Fernando De\u00a0La\u00a0Prieta, Philippe Mathieu, Jaime\u00a0Andr\u00e9s Rinc\u00f3n\u00a0Arango, Alia El\u00a0Bolock, Elena Del\u00a0Val, Jaume Jord\u00e1n\u00a0Prunera, Jo\u00e3o Carneiro, Rub\u00e9n Fuentes, Fernando Lopes, and Vicente Julian (Eds.). Springer International Publishing, Cham, 385\u2013393."},{"key":"e_1_3_2_1_50_1","volume-title":"Retrieved","author":"Sharma Jairam","year":"2022","unstructured":"Jairam Sharma . 2022 . Building Industrial embedded deep learning inference pipelines with TensorRT . Retrieved March 23, 2022 from https:\/\/learnopencv.com\/building-industrial-embedded-deep-learning-inference-pipelines-with-tensorrt\/ Jairam Sharma. 2022. Building Industrial embedded deep learning inference pipelines with TensorRT. 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Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices . In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, NJ, 328\u2013339 . Surat Teerapittayanon, Bradley McDanel, and H.T. Kung. 2017. Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, NJ, 328\u2013339."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.226"},{"key":"e_1_3_2_1_54_1","unstructured":"Rob van\u00a0der Meulen. 2018. What Edge Computing Means for Infrastructure and Operations Leaders. https:\/\/www.gartner.com\/smarterwithgartner\/what-edge-computing-means-for-infrastructure-and-operations-leaders.  Rob van\u00a0der Meulen. 2018. 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