Learning-based run-time power and energy management of multi/many-core systems: current and future trends
| Autor: | Amit Kumar Singh, Karunakar Reddy Basireddy, Geoff V. Merrett, Bashir M. Al-Hashimi, Charles Leech |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
010302 applied physics
Power management Engineering Knowledge management business.industry Energy management Reliability (computer networking) Cloud computing 02 engineering and technology 01 natural sciences 020202 computer hardware & architecture Open research Parallel processing (DSP implementation) Principles of learning 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Systems engineering Reinforcement learning Electrical and Electronic Engineering business |
| Popis: | Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the ever-increasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges. |
| Databáze: | OpenAIRE |
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