Publication | Open Access
Dynamic Voltage and Frequency Scaling in NoCs With Supervised and Reinforcement Learning Techniques
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2019
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Artificial IntelligenceElectrical EngineeringDynamic VoltageEngineeringMachine LearningComplicated Architecture DesignKnowledge DiscoveryComputer EngineeringComputer ArchitectureEmbedded Machine LearningComputer ScienceReinforcement Learning TechniquesComputer ArchitectsLearning ControlNeural Architecture SearchFrequency Scaling
Computer architects often face the challenging task of balancing various design considerations, such as performance, power, cost, and reliability. With its unprecedented success in numerous domains and disciplines, machine learning could be a promising approach to solving complicated architecture design and optimization problems. IEEE Transactions on Computers continues to lead research in this area, recently publishing more than 10 papers that propose innovative, viable, and promising ways to take the advantage of machine learning in computer architecture. Discusses how the IEEE Transactions on Computers will closely follow these exciting developments in applying machine learning to computer architectures and provide the latest academic and industry research.