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Fluchter, \u201cInternet of Things,\u201d Business &amp; Information Systems Engineering, vol.57, no.3, pp.221-224, 2015. 10.1007\/s12599-015-0383-3","DOI":"10.1007\/s12599-015-0383-3"},{"key":"5","unstructured":"[5] Y.C. Hu, M. Patel, D. Sabella, N. Sprecher, and V. Young, \u201cMobile edge computing-a key technology towards 5g,\u201d ETSI White Paper, vol.11, no.11, pp.1-16, 2015."},{"key":"6","unstructured":"[6] \u201cFog computing and the internet of things: Extend the cloud to where the things are,\u201d http:\/\/www.cisco.com\/c\/dam\/en_us\/solutions\/trends\/iot\/docs\/computing-overview.pdf"},{"key":"7","unstructured":"[7] M. Courbariaux, Y. Bengio, and J.-P. David, \u201cBinaryConnect: Training deep neural networks with binary weights during propagations,\u201d Advances in Neural Information Processing Systems 28, pp.3123-3131, 2015."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, \u201cXNOR-Net: ImageNet classification using binary convolutional neural networks,\u201d Computer Vision-ECCV 2016, vol.9908, pp.525-542, 2016. 10.1007\/978-3-319-46493-0_32","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"9","unstructured":"[9] F. Li, B. Zhang, and B. Liu, \u201cTernary weight networks,\u201d 2016. [Online]. Available: https:\/\/arxiv.org\/abs\/1605.04711"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] M.P. Heinrich, M. Blendowski, and O. Oktay, \u201cTernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions,\u201d International Journal of Computer Assisted Radiology and Surgery, vol.13, no.9, pp.1311-1320, 2018. 10.1007\/s11548-018-1797-4","DOI":"10.1007\/s11548-018-1797-4"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] S. Teerapittayanon, B. McDanel, and H.T. Kung, \u201cBranchynet: Fast inference via early exiting from deep neural networks,\u201d 2016 23rd International Conference on Pattern Recognition (ICPR), pp.2464-2469, 2016. 10.1109\/icpr.2016.7900006","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] S. Teerapittayanon, B. McDanel, and H.T. Kung, \u201cDistributed deep neural networks over the cloud, the edge and end devices,\u201d 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp.328-339, 2017. 10.1109\/icdcs.2017.226","DOI":"10.1109\/ICDCS.2017.226"},{"key":"13","unstructured":"[13] \u201cJETSON TX2 high performance AI at the edge.\u201d [Online]. Available: https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/jetson-tx2\/"},{"key":"14","unstructured":"[14] \u201cJETSON NANO the power of modern AI to millions of devices.\u201d [Online]. Available: https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/jetson-nano\/"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] P. Liang, E. Blasch, and H. Ling, \u201cEncoding color information for visual tracking: Algorithms and benchmark,\u201d IEEE Trans. Image Process., vol.24, no.12, pp.5630-5644, 2015. 10.1109\/tip.2015.2482905","DOI":"10.1109\/TIP.2015.2482905"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] Y. Wu, J. Lim, and M.-H. Yang, \u201cOnline object tracking: A benchmark,\u201d 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.2411-2418, 2013. 10.1109\/cvpr.2013.312","DOI":"10.1109\/CVPR.2013.312"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] Y. Wu, J. Lim, and M.-H. Yang, \u201cObject tracking benchmark,\u201d IEEE Trans. Pattern Anal. Mach. 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