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This book focuses on two of the most relevant problems related to power management on multicore and manycore systems. Specifically, one part of the book focuses on maximizing/optimizing computational performance under power or thermal constraints, while another part focuses on minimizing energy consumption under performance (or real-time) constraints. Provides a comprehensive introduction to energy, power, and temperature management, highlighting the different optimization goals, particularly computational performance, power and energy consumption, and temperature; Highlights the differences and similarities between the two key challenges of performance optimization under power or thermal constraints and energy minimization under performance constraints; Discusses in detail several means that can be used to optimize performance or energy while satisfying the desired constraints, including core heterogeneity, task-to-core assignment/mapping, dynamic power management (DPM), and dynamic voltage and frequency scaling (DVFS).

Multiprocessors. --- Engineering. --- Microprocessors. --- Electronics. --- Microelectronics. --- Electronic circuits. --- Circuits and Systems. --- Processor Architectures. --- Electronics and Microelectronics, Instrumentation. --- Electronic digital computers --- Multiprogramming (Electronic computers) --- Parallel processing (Electronic computers) --- Systems engineering. --- Computer science. --- Electrical engineering --- Physical sciences --- Informatics --- Science --- Engineering systems --- System engineering --- Engineering --- Industrial engineering --- System analysis --- Design and construction --- Microminiature electronic equipment --- Microminiaturization (Electronics) --- Electronics --- Microtechnology --- Semiconductors --- Miniature electronic equipment --- Minicomputers --- Electron-tube circuits --- Electric circuits --- Electron tubes

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This book constitutes the refereed proceedings of the 14th International Conference on Similarity Search and Applications, SISAP 2021, held in Dortmund, Germany, in September/October 2021. The conference was held virtually due to the COVID-19 pandemic. The 23 full papers presented together with 5 short and 3 doctoral symposium papers were carefully reviewed and selected from 50 submissions. The papers are organized in the topical sections named: Similarity Search and Retrieval; Intrinsic Dimensionality; Clustering and Classification; Applications of Similarity Search; Similarity Search in Graph-Structured Data; Doctoral Symposium.

Information retrieval --- Programming --- Computer architecture. Operating systems --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- datamining --- applicatiebeheer --- apps --- programmeren (informatica) --- informatiesystemen --- database management --- architectuur (informatica) --- data acquisition

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"Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters. Ranges from embedded systems to large computing clusters. Provides application of the methods in various domains of science and engineering."--Provided by publisher.

SCIENCE / Chemistry / General. --- Artificial Intelligence. --- Big Data and Machine Learning. --- Cyber-physical systems. --- Data mining for Ubiquitous System Software. --- Embedded Systems and Machine Learning. --- Highly Distributed Data. --- ML on Small devices. --- Machine learning for knowledge discovery. --- Machine learning in high-energy physics. --- Resource-Aware Machine Learning. --- Resource-Constrained Data Analysis. --- Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory