Bibliografías temáticas / Heterogeneous embedded system

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Literatura académica sobre el tema "Heterogeneous embedded system"

Autor: Grafiati
Publicado: 11 de marzo de 2023
Última modificación: 14 de marzo de 2023

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Artículos de revistas sobre el tema "Heterogeneous embedded system"

1

Valente, Giacomo, Tiziana Fanni, Carlo Sau, Tania Di Mascio, Luigi Pomante, and Francesca Palumbo. "A Composable Monitoring System for Heterogeneous Embedded Platforms." ACM Transactions on Embedded Computing Systems 20, no. 5 (2021): 1–34. http://dx.doi.org/10.1145/3461647.

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Advanced computations on embedded devices are nowadays a must in any application field. Often, to cope with such a need, embedded systems designers leverage on complex heterogeneous reconfigurable platforms that offer high performance, thanks to the possibility of specializing/customizing some computing elements on board, and are usually flexible enough to be optimized at runtime. In this context, monitoring the system has gained increasing interest. Ideally, monitoring systems should be non-intrusive, serve several purposes, and provide aggregated information about the behavior of the differe
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2

Jammalamadaka, Sastry Kodanda Rama, Valluru Sai Kumar Reddy, and Smt J Sasi Bhanu. "Networking Heterogeneous Microcontroller based Systems through Universal Serial Bus." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 5 (2015): 992. http://dx.doi.org/10.11591/ijece.v5i5.pp992-1002.

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Networking heterogeneous embedded systems is a challenge. Every distributed embedded systems requires that the network is designed specifically considering the heterogeneity that exits among different Microcontroller based systems that are used in developing a distributed embedded system. Communication architecture, which considers the addressing of the individual systems, arbitration, synchronisation, error detection and control etc., needs to be designed considering a specific application. The issue of configuring the slaves has to be addressed. It is also important that the messages, flow o
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3

Zhang, Huafeng, Hehua Zhang, Ming Gu, and Jiaguang Sun. "Modeling a Heterogeneous Embedded System in Coloured Petri Nets." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/943094.

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Embedded devices are everywhere now and, unlike personal computers, their systems differ in implementation languages and behaviors. Interactions of different devices require programmers to master programming paradigms in all related languages. So, a defect may occur if differences in systems' behaviors are ignored. In this paper, a heterogeneous system which is composed of two subsystems is introduced and we point out a potential defect in this system caused by an interface mismatch. Then, a state based approach is applied to verify our analysis of the system.
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4

Syschikov, Alexey, Yuriy Sheynin, Boris Sedov, and Vera Ivanova. "Domain-Specific Programming Environment for Heterogeneous Multicore Embedded Systems." International Journal of Embedded and Real-Time Communication Systems 5, no. 4 (2014): 1–23. http://dx.doi.org/10.4018/ijertcs.2014100101.

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Nowadays embedded systems are used in a broad range of domains such as avionics, space, automotive, mobile, domestic appliances etc. Sophisticated software determines the quality of embedded systems and requires high-qualified experts for software development. Software becomes the main assert of embedded systems that is valuable to retain in changing computing platforms in embedded systems evolution. Computing platforms for embedded systems became multicore processors and SoC, they can change in the embedded system lifetime that could be long (dozen of years for an automobile and airplane). It
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5

Wu, Dian Hong. "Task Optimization Scheduling Algorithm in Embedded System Based on Internet of Things." Applied Mechanics and Materials 513-517 (February 2014): 2398–402. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2398.

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Embedded system has been widely used in the network, server, etc., and it has a good application prospect with the development of Internet of things. In the embedded heterogeneous computing system, task scheduling is the key to deciding the system performance. For multi-task scheduling, the current scheduling algorithm is mostly based on task duplication, without a full consideration of the correlation between the predecessor task and its subsequent tasks. Based on modeling the multi-frame task scheduling problem in the heterogeneous embedded system, this paper analyzes the availability of tas
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6

Pervan, Branimir, Josip Knezović, and Emanuel Guberović. "Energy-efficient distributed password hash computation on heterogeneous embedded system." Automatika 63, no. 3 (2022): 399–417. http://dx.doi.org/10.1080/00051144.2022.2042115.

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7

Rath, A. K., and S. N. Dehuri. "Non-dominated Sorting Genetic Algorithms for Heterogeneous Embedded System Design." Journal of Computer Science 2, no. 3 (2006): 288–91. http://dx.doi.org/10.3844/jcssp.2006.288.291.

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E, Mounika. "A Hybridised Heterogeneous Embedded System Networking through Multi-Master Interface." International Journal of Emerging Trends in Engineering Research 8, no. 3 (2020): 885–93. http://dx.doi.org/10.30534/ijeter/2020/45832020.

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9

Majumdar, Abhinandan, Srihari Cadambi, and Srimat T. Chakradhar. "An Energy-Efficient Heterogeneous System for Embedded Learning and Classification." IEEE Embedded Systems Letters 3, no. 1 (2011): 42–45. http://dx.doi.org/10.1109/les.2010.2100802.

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10

Campeanu, Gabriel, and Mehrdad Saadatmand. "A Two-Layer Component-Based Allocation for Embedded Systems with GPUs." Designs 3, no. 1 (2019): 6. http://dx.doi.org/10.3390/designs3010006.

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Component-based development is a software engineering paradigm that can facilitate the construction of embedded systems and tackle its complexities. The modern embedded systems have more and more demanding requirements. One way to cope with such a versatile and growing set of requirements is to employ heterogeneous processing power, i.e., CPU–GPU architectures. The new CPU–GPU embedded boards deliver an increased performance but also introduce additional complexity and challenges. In this work, we address the component-to-hardware allocation for CPU–GPU embedded systems. The allocation for suc
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