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Mohammed Suleiman Mohammed Rudwan and Jean Vincent Fonou-Dombeu
Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies rem...
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Carmine Paolino, Alessio Antolini, Francesco Zavalloni, Andrea Lico, Eleonora Franchi Scarselli, Mauro Mangia, Alex Marchioni, Fabio Pareschi, Gianluca Setti, Riccardo Rovatti, Mattia Luigi Torres, Marcella Carissimi and Marco Pasotti
Analog In-Memory computing (AIMC) is a novel paradigm looking for solutions to prevent the unnecessary transfer of data by distributing computation within memory elements. One such operation is matrix-vector multiplication (MVM), a workhorse of many fiel...
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Aleksandr Romanov
This article presents software for the synthesis of circulant graphs and the dataset obtained. An algorithm and new methods, which increase the speed of finding optimal circulant topologies, are proposed. The results obtained confirm an increase in perfo...
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Chi Gao, Xiaofei Xu, Zhizou Yang, Liwei Lin and Jian Li
In recent decades, memory-intensive applications have experienced a boom, e.g., machine learning, natural language processing (NLP), and big data analytics. Such applications often experience out-of-memory (OOM) errors, which cause unexpected processes t...
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Alexander Feoktistov, Alexei Edelev, Andrei Tchernykh, Sergey Gorsky, Olga Basharina and Evgeniy Fereferov
Implementing high-performance computing (HPC) to solve problems in energy infrastructure resilience research in a heterogeneous environment based on an in-memory data grid (IMDG) presents a challenge to workflow management systems. Large-scale energy inf...
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Kévin Mambu, Henri-Pierre Charles, Maha Kooli and Julie Dumas
In-memory computing (IMC) aims to solve the performance gap between CPU and memories introduced by the memory wall. However, it does not address the energy wall problem caused by data transfer over memory hierarchies. This paper proposes the data-localit...
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Md. Oli-Uz-Zaman, Saleh Ahmad Khan, Geng Yuan, Zhiheng Liao, Jingyan Fu, Caiwen Ding, Yanzhi Wang and Jinhui Wang
When deep neural network (DNN) is extensively utilized for edge AI (Artificial Intelligence), for example, the Internet of things (IoT) and autonomous vehicles, it makes CMOS (Complementary Metal Oxide Semiconductor)-based conventional computers suffer f...
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Atousa Jafari, Christopher Münch and Mehdi Tahoori
Computing data-intensive applications on the von Neumann architecture lead to significant performance and energy overheads. The concept of computation in memory (CiM) addresses the bottleneck of von Neumann machines by reducing the data movement in the c...
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K. Haritha, M. V. Judy, Konstantinos Papageorgiou, Vassilis C. Georgiannis and Elpiniki Papageorgiou
The features of a dataset play an important role in the construction of a machine learning model. Because big datasets often have a large number of features, they may contain features that are less relevant to the machine learning task, which makes the p...
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Tommaso Zanotti, Francesco Maria Puglisi and Paolo Pavan
Different in-memory computing paradigms enabled by emerging non-volatile memory technologies are promising solutions for the development of ultra-low-power hardware for edge computing. Among these, SIMPLY, a smart logic-in-memory architecture, provides h...
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