Titlebook: Artificial Intelligence for Edge Computing; Mudhakar Srivatsa,Tarek Abdelzaher,Ting He Book 2023 The Editor(s) (if applicable) and The Aut

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Termination and Well-Foundedness,d resources of edge devices. Many traditional AI models are designed for large-scale cloud environments with ample GPUs. The computational environment at the edge is substantially different. Specifically, it is much more resource-constrained. Fortunately, often edge applications are also more restri

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https://doi.org/10.1007/978-3-658-12163-1al approaches have been proposed to mitigate this issue, using gradient compression and infrequent communication based techniques. This chapter summarizes two communication efficient algorithms, . and ., for . and . settings, respectively. These algorithms utilize . sparsification and quantization o

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Warum sollten Sie dieses Buch lesen?,ditional data compression schemes that aim at supporting the reconstruction of the original data, here the compression only needs to support the learning of the models that need to be learned from the original data, in order to support AI applications in a bandwidth-limited edge network. This lowere

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https://doi.org/10.1007/978-94-011-0131-8ms. To have the maximum applicability, the machine learning workloads will be simply modeled as demands for various types of resources (storage, communication, computation), and the resource allocation algorithms are designed to optimally satisfy these demands within the limited resource capacities

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https://doi.org/10.1007/978-3-319-12799-6, for running DNN-based perception models in real time on resource-constraint edge platforms to process the sensing data stream (i.e., sequence of image frames). Mainstream machine inference frameworks commonly adopt a simple First-in-First-out (FIFO) policy to process the perceived images in a holi

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