Human activity detection from sensor data has developed as a critical study subject with far-reaching implications in healthcare, sports, security, and beyond. This study proposes a unique way to reliably detect and c...
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Machine learning and deep learning algorithms have the potential to revolutionize network data analytics in 5G cellular networks. With the increase in the number of connected devices and the explosion of data generate...
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The first international workshop on Quantum data Science and Management (QDSM), co-located with VLDB 2023, is centered around addressing the possibilities of quantum computing for data science and data management. Qua...
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Analog computing-in-memory (ACiM) technology has shown strong potential for neural network accelerators, addressing von-Neumann performance bottlenecks with in-memory dataprocessing and computation. Understanding the...
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ISBN:
(数字)9798400706318
ISBN:
(纸本)9798400706318
Analog computing-in-memory (ACiM) technology has shown strong potential for neural network accelerators, addressing von-Neumann performance bottlenecks with in-memory dataprocessing and computation. Understanding the ACiM design space, including its trade-offs and constraints, and systematically and effectively exploring it for optimal performance is essential to turn the promise into a viable product. Recent research demonstrated that multi-objective searches for ACiM architectures with heterogeneous tiles can simultaneously optimize power, performance, and area (PPA), outperforming existing tiled ACiM proposals. In this paper, we propose NavCim, a comprehensive ACiM design space exploration mechanism that advances the prior work in terms of search efficiency, search space coverage, and optimization metrics. NavCim introduces predictive modeling of ACiM hardware performance and uses the PPA prediction models instead of running simulators, significantly reducing search overheads. Faster searches enable NavCim to extend the architecture and model search spaces with an evolutionary search process to optimize architectures with more than two different tile sizes for multiple input models. With accuracy-aware searches, NavCim considers PPA and model accuracy together as optimization goals to achieve more balanced trade-offs. The experimental searches show that NavCim leverages predictive models to reduce search time by up to 7.3x without compromising the quality of search results. It also successfully identifies heterogeneous ACiM architectures that can efficiently execute multiple models on a single chip, improving accuracy by up to 19% over the prior work.
I introduce PieRank, a library aimed at embedded large-scale sparse matrix processing. It enjoys significant advantages over previous state-of-the-art for handling big, sparse data sets in scalability, speed, and usab...
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Vehicle clustering has received substantial attention and has undergone extensive research as a promising approach to enhancing network stability, reliability, and scalability. However, there still exists a significan...
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ISBN:
(纸本)9798350313062;9798350313079
Vehicle clustering has received substantial attention and has undergone extensive research as a promising approach to enhancing network stability, reliability, and scalability. However, there still exists a significant deficiency in achieving a clear comprehension of how to develop an optimal clustering approach. This paper addresses the problem of optimal multi-hop clustering and presents a solution to maximize the stability of clusters. An efficient greedy algorithm is developed, which by considering vehicle states over a prediction horizon, identifies a set of cluster heads and their designated cluster members. We leverage the capabilities of Vehicular Edge computing servers and implement a load-balancing approach that distributes computation tasks among the VEC servers to prevent the overloading of any single server. A data aggregation approach based on Adaptive Huffman Coding is also presented, which by identifying and eliminating duplicate data, reduces the volume of clustering data transmitted to VEC servers. For evaluation purposes, we use authentic traffic data generated from real-world floating car data. The results demonstrate that our multi-hop clustering algorithm outperforms the alternative algorithms in terms of cluster head duration, cluster member duration, and cluster head change rate under various traffic conditions and with varying maximum numbers of hops. The impacts of data aggregation on packet collision and redundancy rates are also investigated as a function of data aggregation interval.
In distributed cloud computing environments, replication of data is a crucial technique for achieving high availability and reliability. However, optimizing replication of data poses significant challenges due to the ...
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Cloud computing is the most popular way of delivering on-demand computational resources. Recently, the research in this area has started to focus on carbon-aware clouds. Here, the most challenging aspects are related ...
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ISBN:
(纸本)9783031585012;9783031585029
Cloud computing is the most popular way of delivering on-demand computational resources. Recently, the research in this area has started to focus on carbon-aware clouds. Here, the most challenging aspects are related to defining strategies for efficient task scheduling and resource allocation. These strategies can be simulated and assessed using dedicated tools. However, to perform their accurate evaluation, the tests should reproduce close-to-real conditions of the actual cloud center. In particular, they require running simulations with various mixtures of tasks that replicate the actual cloud center operation. Therefore, the main aim of this work was to prepare tools that will allow the generation of synthetic job streams, with mixes of realistic types of computational tasks. The core of this contribution is the analysis of actual job processingdata from the CloudFerro cloud center. The proposed methodology is based on data clustering and includes a comparison between multiple algorithms. Furthermore, the resulting clusters have been categorized from the point of view of cloud center operation, in order to identify prototypical tasks' classes with respect to the resource demands. Finally, a tool that generates synthetic job streams, based on the Gaussian Mixture Model, which has been implemented, is summarized.
During an observation season, for the representative of the transportation infrastructure, the intelligent monitoring of strain, deformation, cable force and mode of the urban river-crossing super large bridge will be...
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The current traditional data distribution algorithms lead to poor distribution due to the lack of mapping processing of data. In this regard, the research of data distribution based on AHM attribute hierarchy model is...
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