posted Nov 15, 2020, 1:53 AM by MUHAMMAD MUN`IM AHMAD ZABIDI
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updated Nov 15, 2020, 1:55 AM
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Table 3 from M. G. S. Murshed, C. Murphy, D. Hou, N. Khan, G. Ananthanarayanan, and F. Hussain, “Machine Learning at the Network Edge: A Survey,” arXiv Prepr. arXiv1908.00080, pp. 1–28, 2019.
Framework | Core language | Interface | Part running on the edge | Example applications | TensorFlow Lite (Google) | C++ Java | C/C++ Java | TensorFlow Lite NN API | computer vision [109], speech recognition [42, 1] | Caffe2 Caffe2Go (Facebook) | C++ | Android iOs | NNPack | image analysis, video analysis [53] | Apache MXNet | C++ Python R | Linux MacOS Windows | Full Model | object detection, recognition [78] | Core ML2 (Apple) | Python | iOS | CoreML | image analysis [16] NLP [105] | ML Kit (Google) | C++ Java | Android iOs | Full Model | image recognition, text recognition, bar-code scaning [26] | AI2GO | C, Python Java, Swift | Linux macOs | Full Model | object detection, classification [5] | DeepThings | C/C++ | Linux | Full Model | object detection [119] | DeepIoT | Python | Ubilinux | Full Model | human activity recognition, user identification [116] | DeepCham | C++ Java | Linux Android | Full Model | object recognition [62] | SparseSep | - | Linux Android | Full Model | mobile object recognition, audio classification [15] | Edgent | - | Ubuntu | Major part of the DNN | image recognition [63] |
17 |
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