The future of Moore's Law is in jeopardy. The number of cores of many-core systems is steadily increasing for every technology node generation. Voltage scaling does not keep pace with the unabated decrease of transistor size. Higher leakage power and manufacturing variabilities are the consequences and lead to extremely critical power as well as thermal issues. These phenomena can downgrade the performance or endanger system's functionality as well as its reliability if they are not properly addressed. In near future, up to 90% of a many-core chip's area may have to remain inactive; this non-active area is termed Dark Silicon. These issues make the problem of resource management challenging. Future management systems need to be intelligent, anticipatory, and self-adaptive. They are supposed to integrate management of different aspects such as thermal, power, energy, performance, quality of service, process variability, occurrence of faults and aging effects, all in one. In this paper, we study the contributions in the literature focusing on techniques for dynamic resource management in multi-and many-core systems. We put emphasis on advanced approaches that exhibit learning, self-awareness, hierarchical monitoring and management. We categorize the existing approaches from a new perspective and argue that a self-aware hierarchical agent-based model is a proper methodology to monitor and management many-core systems, in particular when they need to deal with different competing goals. In addition, we evaluate the main objectives and trends in resource management of many-core systems in order to pave the way for designing future computer systems ranging from highperformance computers to embedded processors used in the era of Internet-of-Things.
Abstract-In modern industrial production fast and easily reconfigurable transportation systems are necessary. A viable bioinspired approach to this is throwing and catching of transportation goods. In order to catch a thrown object the catching device has to be moved to the right position on time. This requires a fast and accurate acquisition of flight position and a prediction system for the interception point. The main topic of this paper is the development and comparison of two prediction models for the flight trajectory of a thrown tennis ball. The position acquisition, that is the base for the prediction, is based on a binocular vision system similar to two-eyed humans. The impact of the vision systems frame rate on the error of the prediction is reviewed as well. Future prediction is planned to be done based on a bio-inspired approach using a small set of reference throws.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.