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Outlines modern research directions for pre-silicon power modeling and post-silicon power characterization. An invaluable reference for anyone with an interest in power consumption and how it affects the performance of future computing systems.
Outlines modern research directions for pre-silicon power modeling and post-silicon power characterization. An invaluable reference for anyone with an interest in power consumption and how it affects the performance of future computing systems.
This book describes for readers a methodology for dynamic power estimation, using Transaction Level Modeling (TLM). The methodology exploits the existing tools for RTL simulation, design synthesis and SystemC prototyping to provide fast and accurate power estimation using Transaction Level Power Modeling (TLPM). Readers will benefit from this innovative way of evaluating power on a high level of abstraction, at an early stage of the product life cycle, decreasing the number of the expensive design iterations.
The six volumes LNCS 11619-11624 constitute the refereed proceedings of the 19th International Conference on Computational Science and Its Applications, ICCSA 2019, held in Saint Petersburg, Russia, in July 2019. The 64 full papers, 10 short papers and 259 workshop papers presented were carefully reviewed and selected form numerous submissions. The 64 full papers are organized in the following five general tracks: computational methods, algorithms and scientific applications; high performance computing and networks; geometric modeling, graphics and visualization; advanced and emerging applications; and information systems and technologies. The 259 workshop papers were presented at 33 workshops in various areas of computational sciences, ranging from computational science technologies to specific areas of computational sciences, such as software engineering, security, artificial intelligence and blockchain technologies.
This book constitutes the thoroughly refereed proceedings of the 39th National Conference of Theoretical Computer Science, NCTCS 2021, held in Yinchuan, China, in July 2021. The 67 full papers were carefully reviewed and selected from 145 submissions, and 14 of them were selected for the volume. The papers present recent research in the areas of information hiding, data detection and recognition, system scheduling, time series prediction, and formal analysis.
Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. - Presents advanced optimization techniques to improve existing energy demand system - Provides data-analytic models and their practical relevance in proven case studies - Explores novel developments in machine-learning and artificial intelligence applied in energy management - Provides modeling theory in an easy-to-read format