If an edge-node orchestrator can partition Big Data tasks of variable computational complexity between the edge and cloud resources, major reductions in total task completion times can be achieved even at low Wide Are...
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When dealing with high dimensional sparse data, such as in recommender systems,co-clusteringturnsouttobemorebeneficialthanone-sidedclustering,even if one is interested in clustering along one dimension only. Thereby, ...
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From the world wide web, to genomics, to traffic analysis, graphs are central to many scientific, engineering, and societal endeavours. Therefore an important question is what hardware technologies are most appropriat...
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This paper presents a novel approach to writing TOSCA templates for application reusability and portability in a modular auto-scaling and orchestration framework (MiCADO). The approach defines cloud resources as well ...
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A growing focus is on optimizing learning outcomes by supporting metacognitive monitoring, enabling learners to self-assess and adjust their learning strategies. Our goal is to support children in enhancing their meta...
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Transformer models have become a cornerstone of various natural language processing(NLP)***,the substantial computational overhead during the inference remains a significant challenge,limiting their deployment in prac...
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Transformer models have become a cornerstone of various natural language processing(NLP)***,the substantial computational overhead during the inference remains a significant challenge,limiting their deployment in practical *** this study,we address this challenge by minimizing the inference overhead in transformer models using the controlling element on artificial intelligence(AI)*** work is anchored by four key ***,we conduct a comprehensive analysis of the overhead composition within the transformer inference process,identifying the primary ***,we leverage the management processing element(MPE)of the Shenwei AI(SWAI)accelerator,implementing a three-tier scheduling framework that significantly reduces the number of host-device launches to approximately 1/10000 of the original PyTorch-GPU ***,we introduce a zero-copy memory management technique using segment-page fusion,which significantly reduces memory access latency and improves overall inference ***,we develop a fast model loading method that eliminates redundant computations during model verification and initialization,reducing the total loading time for large models from 22128.31 ms to 1041.72 *** contributions significantly enhance the optimization of transformer models,enabling more efficient and expedited inference processes on AI accelerators.
Reference architectures for big data and machine learning include not only interconnected building blocks but important considerations (among others) for scalability, manageability and usability issues as well. Levera...
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We develop several deep learning algorithms for approximating families of parametric PDE solutions. The proposed algorithms approximate solutions together with their gradients, which in the context of mathematical fin...
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As cloud adoption increases, so do the number of available cloud service providers. Moving complex applications between clouds can be beneficial—or other times necessary—but achieving this so-called cloud portabilit...
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Recognizing toponyms and resolving them to their real-world referents is required to provide advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candid...
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