Search results for "Ring network"

showing 2 items of 12 documents

Fault-Tolerant Application-Specific Topology-Based NoC and Its Prototype on an FPGA

2021

Application-Specific Networks-on-Chips (ASNoCs) are suitable communication platforms for meeting current application requirements. Interconnection links are the primary components involved in communication between the cores of an ASNoC design. The integration density in ASNoC increases with continuous scaling down of the transistor size. Excessive integration density in ASNoC can result in the formation of thermal hotspots, which can cause a system to fail permanently. As a result, fault-tolerant techniques are required to address the permanent faults in interconnection links of an ASNoC design. By taking into account link faults in the topology, this paper introduces a fault-tolerant appli…

RouterGeneral Computer ScienceComputer scienceHeuristic (computer science)Topology (electrical circuits)02 engineering and technologyTopologyNetwork topology01 natural sciencescommunication latencySoftware0103 physical sciences0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceNetwork-on-ChipField-programmable gate arrayFPGA010302 applied physicsbusiness.industryGeneral EngineeringRing networkFault tolerancefault-toleranceTK1-9971020202 computer hardware & architectureVDP::Teknologi: 500Electrical engineering. Electronics. Nuclear engineeringbusinessspare linkapplication-specific designIEEE Access
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Active Learning for Monitoring Network Optimization

2012

Kernel-based active learning strategies were studied for the optimization of environmental monitoring networks. This chapter introduces the basic machine learning algorithms originated in the statistical learning theory of Vapnik (1998). Active learning is closer to an optimization done using sequential Gaussian simulations. The chapter presents the general ideas of statistical learning from data. It derives the basics of kernel-based support vector algorithms. The active learning framework is presented and machine learning extensions for active learning are described in the chapter. Kernel-based active learning strategies are tested on real case studies. The chapter explores the use of a c…

Active learningComputer scienceActive learning (machine learning)Kernel-based support vector algorithmsMachine learningGaussian simulationsData scienceMonitoring network optimization
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