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Learning transfer-based adaptive energy minimization in embedded systems

Learning transfer-based adaptive energy minimization in embedded systems
Learning transfer-based adaptive energy minimization in embedded systems
Embedded systems execute applications with different performance requirements. These applications exercise the hardware differently depending on the types of computation being carried out, generating varying workloads with time. We will demonstrate that 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 energy minimization approach, capable of minimizing energy through adaptation to these variations. At the core of the approach is an initial learning through reinforcement learning algorithm that suitably selects the appropriate voltage/frequency scalings (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 system application, runtime and hardware layers to adjust the power control levers. The proposed approach is implemented as a power governor in Linux and 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 existing approaches. Scaling the approach further to multi-core systems, we also show that it can minimize energy by up to 18% with 2X reduction in the learning time when compared with a recently reported approach.
energy efficiency, machine learning, DVFS
University of Southampton
SHAFIK, RISHAD A
aa0bdafc-b022-4cb2-a8ef-4bf8a03ba524
Yang, Sheng
04b9848f-ddd4-4d8f-93b6-b91a2144d49c
Das, Anup
2a0d6cea-309b-4053-a62e-234807f89306
Maeda-Nunez, Luis, Alfonso
75dd521f-c2c0-4006-90d0-9ae32a3534cf
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
SHAFIK, RISHAD A
aa0bdafc-b022-4cb2-a8ef-4bf8a03ba524
Yang, Sheng
04b9848f-ddd4-4d8f-93b6-b91a2144d49c
Das, Anup
2a0d6cea-309b-4053-a62e-234807f89306
Maeda-Nunez, Luis, Alfonso
75dd521f-c2c0-4006-90d0-9ae32a3534cf
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d

SHAFIK, RISHAD A, Yang, Sheng, Das, Anup, Maeda-Nunez, Luis, Alfonso, Merrett, Geoffrey and Al-Hashimi, Bashir (2015) Learning transfer-based adaptive energy minimization in embedded systems. University of Southampton doi:10.5258/SOTON/383899 [Dataset]

Record type: Dataset

Abstract

Embedded systems execute applications with different performance requirements. These applications exercise the hardware differently depending on the types of computation being carried out, generating varying workloads with time. We will demonstrate that 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 energy minimization approach, capable of minimizing energy through adaptation to these variations. At the core of the approach is an initial learning through reinforcement learning algorithm that suitably selects the appropriate voltage/frequency scalings (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 system application, runtime and hardware layers to adjust the power control levers. The proposed approach is implemented as a power governor in Linux and 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 existing approaches. Scaling the approach further to multi-core systems, we also show that it can minimize energy by up to 18% with 2X reduction in the learning time when compared with a recently reported approach.

Spreadsheet
Results_all.xlsx - Dataset
Available under License Creative Commons Attribution.
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More information

Published date: 2015
Keywords: energy efficiency, machine learning, DVFS
Organisations: Electronics & Computer Science, Electronic & Software Systems, Faculty of Physical Sciences and Engineering
Projects:
PRiME: Power-efficient, Reliable, Many-core Embedded systems
Funded by: UNSPECIFIED (EP/K034448/1)
13 May 2013 to 12 May 2018

Identifiers

Local EPrints ID: 383899
URI: http://eprints.soton.ac.uk/id/eprint/383899
PURE UUID: 20f8ec52-7b38-4a15-b4c6-4310b2cc0c92
ORCID for Geoffrey Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 12 Nov 2015 16:49
Last modified: 05 Nov 2023 02:40

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Contributors

Creator: RISHAD A SHAFIK
Creator: Sheng Yang
Creator: Anup Das
Creator: Luis, Alfonso Maeda-Nunez
Creator: Geoffrey Merrett ORCID iD
Creator: Bashir Al-Hashimi

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