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Learning Transfer-Based Adaptive Energy Minimization in Embedded Systems
Journal article   Open access   Peer reviewed

Learning Transfer-Based Adaptive Energy Minimization in Embedded Systems

Rishad A. Shafik, Sheng Yang, Anup Das, Luis A. Maeda-Nunez, Geoff V. Merrett and Bashir M. Al-Hashimi
IEEE transactions on computer-aided design of integrated circuits and systems, v 35(6), pp 877-890
01 Jun 2016
DOI: https://doi.org/10.1109/TCAD.2015.2481867
url
https://eprints.soton.ac.uk/374893/1/tcad2015-eprints.pdfView
Accepted (AM)Open Access (License Unspecified),  Open

Abstract

Computer Science Computer Science, Hardware & Architecture Computer Science, Interdisciplinary Applications Engineering Engineering, Electrical & Electronic Science & Technology Technology
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.

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49 citations in Web of Science
59 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Hardware & Architecture
Computer Science, Interdisciplinary Applications
Engineering, Electrical & Electronic
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