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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.
1-14
Shafik, Rishad Ahmed
aa0bdafc-b022-4cb2-a8ef-4bf8a03ba524
Das, Anup K.
2a0d6cea-309b-4053-a62e-234807f89306
Maeda-Nunez, Luis Alfonso
26a9ecdf-102d-41f1-8997-0a037fa087f0
Yang, Sheng
04b9848f-ddd4-4d8f-93b6-b91a2144d49c
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Shafik, Rishad Ahmed
aa0bdafc-b022-4cb2-a8ef-4bf8a03ba524
Das, Anup K.
2a0d6cea-309b-4053-a62e-234807f89306
Maeda-Nunez, Luis Alfonso
26a9ecdf-102d-41f1-8997-0a037fa087f0
Yang, Sheng
04b9848f-ddd4-4d8f-93b6-b91a2144d49c
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d

Shafik, Rishad Ahmed, Das, Anup K., Maeda-Nunez, Luis Alfonso, Yang, Sheng, Merrett, Geoff V. and Al-Hashimi, Bashir (2015) Learning transfer-based adaptive energy minimization in embedded systems IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-14.

Record type: Article

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.

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More information

Accepted/In Press date: 16 August 2015
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 374893
URI: http://eprints.soton.ac.uk/id/eprint/374893
PURE UUID: 01fb4214-1396-4ed2-b07f-d3f5990ceaa0
ORCID for Geoff V. Merrett: ORCID iD orcid.org/0000-0003-4980-3894

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Date deposited: 05 Mar 2015 09:37
Last modified: 24 Jul 2017 16:39

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Contributors

Author: Rishad Ahmed Shafik
Author: Anup K. Das
Author: Luis Alfonso Maeda-Nunez
Author: Sheng Yang
Author: Geoff V. Merrett ORCID iD

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