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Titlebook: Near Threshold Computing; Technology, Methods Michael Hübner,Cristina Silvano Book 2016 Springer International Publishing Switzerland 2016

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發(fā)表于 2025-3-25 07:12:56 | 只看該作者
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Variability-Aware Voltage Island Management for Near-Threshold Computing with Performance Guaranteesage and frequency allocation schemes. We propose three aggressive NTC voltage tuning and allocation strategies, showing that performance can be efficiently sustained or even optimized at the NTC regime. Finally, we show that NTC highly depends on the underlying workload characteristics, delivering a
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發(fā)表于 2025-3-26 00:08:04 | 只看該作者
so that each region of the chip can operate at the most efficient voltage and frequency point;.·???????? Investigates how performance guarantees can be ensured when moving towards NTC manycores through variability-aware voltage and frequency allocation schemes..978-3-319-37209-9978-3-319-23389-5
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發(fā)表于 2025-3-26 06:46:05 | 只看該作者
time are significantly influenced by the order of adding activities. In this paper, we investigate the effect of different ordering strategies on the discovered models (w.r.t. fitness and precision) and the computation time using real-life event data. The results show that the proposed ordering stra
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發(fā)表于 2025-3-26 09:31:50 | 只看該作者
h specific modality combination.. The results provide the following conclusions: First, the machine learning in the recent years plays an important role in the automatic registration process. An important number of research propose a learning-based registration solution. Second, There few solutions
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發(fā)表于 2025-3-26 16:11:11 | 只看該作者
Shekhar Borkartypo based on masked language model output, character-level similarities, and edit distance. Exploiting the combination of the masked language model, character-level similarities, and edit distance assists us in recommending similar context-related candidates. We have used recall (correction rate) a
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發(fā)表于 2025-3-26 20:49:24 | 只看該作者
, and the necessity for system clarity and interpretability. Through a comprehensive examination, we unveil not only innovative strategies for integrating Language Models (LLMs) with Information Retrieval (IR) systems, but also the consequential considerations that underline the need for a balanced
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