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Published in:
2018 | OriginalPaper | Chapter
7. Run-Time Adaptations for Lifetime Improvement
Authors : Anup Kumar Das, Akash Kumar, Bharadwaj Veeravalli, Francky Catthoor
Published in: Reliable and Energy Efficient Streaming Multiprocessor Systems
Publisher: Springer International Publishing
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Abstract
As discussed in previous chapters, energy consumption and reliability are two important optimization objectives for multiprocessor systems. There is a strong interplay between these two objective functions. Reducing the temperature of a system by efficient thermal management leads to a reduction of leakage power. On the other hand, reduction of power dissipation (by controlling the voltage and frequency of operation) leads to an improvement in the thermal profile of a system. However, too frequent voltage and frequency scaling increases thermal cycles, which increases stress in the metal layers causing reliability concerns. This has attracted significant attention in recent years to investigate on intelligent techniques, such as the use of machine learning, to determine the relationship between temperature, energy and performance and their control using voltage and frequency switching. State-of-the-art learning-based approaches suffer from the following limitations.