Journal article
Learning Transfer-Based Adaptive Energy Minimization in Embedded Systems
IEEE transactions on computer-aided design of integrated circuits and systems, v 35(6), pp 877-890
01 Jun 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Embedded systems execute applications with varying performance requirements. These applications exercise the hardware differently depending on the computation task, generating varying workloads with time. Energy minimization with such workload and performance variations within (intra) and across (inter) applications is particularly challenging. To address this challenge, we propose an online approach, capable of minimizing energy through adaptation to these variations. At the core of this approach is a reinforcement learning algorithm that suitably selects the appropriate voltage/frequency scaling (VFS) based on workload predictions to meet the applications' performance requirements. The adaptation is then facilitated and expedited through learning transfer, which uses the interaction between the application, runtime, and hardware layers to adjust the VFS. The proposed approach is implemented as a power governor in Linux and extensively validated on an ARM Cortex-A8 running different benchmark applications. We show that with intra-and inter-application variations, our proposed approach can effectively minimize energy consumption by up to 33% compared to the existing approaches. Scaling the approach to multicore systems, we also demonstrate that it can minimize energy by up to 18% with 2x reduction in the learning time when compared with an existing approach.
Metrics
Details
- Title
- Learning Transfer-Based Adaptive Energy Minimization in Embedded Systems
- Creators
- Rishad A. Shafik - Newcastle UniversitySheng Yang - University of SouthamptonAnup Das - University of SouthamptonLuis A. Maeda-Nunez - University of SouthamptonGeoff V. Merrett - University of SouthamptonBashir M. Al-Hashimi - University of Southampton
- Publication Details
- IEEE transactions on computer-aided design of integrated circuits and systems, v 35(6), pp 877-890
- Publisher
- IEEE
- Number of pages
- 14
- Grant note
- EP/K034448/1 / Engineering and Physical Sciences Research Council; UK Research & Innovation (UKRI); Engineering & Physical Sciences Research Council (EPSRC) EP/K034448/1 / EPSRC; UK Research & Innovation (UKRI); Engineering & Physical Sciences Research Council (EPSRC) EP/K034448/1 / Engineering and Physical Science Research Council, U.K., under (PRiME Project); UK Research & Innovation (UKRI); Engineering & Physical Sciences Research Council (EPSRC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000377105700001
- Scopus ID
- 2-s2.0-84971324243
- Other Identifier
- 991019295196404721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Interdisciplinary Applications
- Engineering, Electrical & Electronic