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Execution trace--driven energy-reliability optimization for multimedia MPSoCs

Execution trace--driven energy-reliability optimization for multimedia MPSoCs
Execution trace--driven energy-reliability optimization for multimedia MPSoCs
Multiprocessor systems-on-chip (MPSoCs) are becoming a popular design choice in current and future technology nodes to accommodate the heterogeneous computing demand of a multitude of applications enabled on these platform. Streaming multimedia and other communication-centric applications constitute a significant fraction of the application space of these devices. The mapping of an application on an MPSoC is an NP-hard problem. This has attracted researchers to solve this problem both as stand-alone (best-effort) and in conjunction with other optimization objectives, such as energy and reliability. Most existing studies on energy-reliability joint optimization are static—that is, design time based. These techniques fail to capture runtime variability such as resource unavailability and dynamism associated with application behaviors, which are typical of multimedia applications. The few studies that consider dynamic mapping of applications do not consider throughput degradation, which directly impacts user satisfaction. This article proposes a runtime technique to analyze the execution trace of an application modeled as Synchronous Data Flow Graphs (SDFGs) to determine its mapping on a multiprocessor system with heterogeneous processing units for different fault scenarios. Further, communication energy is minimized for each of these mappings while satisfying the throughput constraint. Experiments conducted with synthetic and real SDFGs demonstrate that the proposed technique achieves significant improvement with respect to the state-of-the-art approaches in terms of throughput and storage overhead with less than 20% energy overhead.
1936-7406
Das, Anup K.
2a0d6cea-309b-4053-a62e-234807f89306
Singh, Amit K.
4ade41b4-b0dc-4aee-9a61-16c0716fc350
Kumar, Akash
3e1191e9-dc51-4f9e-8e47-80524db219dc
Das, Anup K.
2a0d6cea-309b-4053-a62e-234807f89306
Singh, Amit K.
4ade41b4-b0dc-4aee-9a61-16c0716fc350
Kumar, Akash
3e1191e9-dc51-4f9e-8e47-80524db219dc

Das, Anup K., Singh, Amit K. and Kumar, Akash (2015) Execution trace--driven energy-reliability optimization for multimedia MPSoCs. ACM Transactions on Reconfigurable Technology and Systems, 8 (18). (doi:10.1145/2665071).

Record type: Article

Abstract

Multiprocessor systems-on-chip (MPSoCs) are becoming a popular design choice in current and future technology nodes to accommodate the heterogeneous computing demand of a multitude of applications enabled on these platform. Streaming multimedia and other communication-centric applications constitute a significant fraction of the application space of these devices. The mapping of an application on an MPSoC is an NP-hard problem. This has attracted researchers to solve this problem both as stand-alone (best-effort) and in conjunction with other optimization objectives, such as energy and reliability. Most existing studies on energy-reliability joint optimization are static—that is, design time based. These techniques fail to capture runtime variability such as resource unavailability and dynamism associated with application behaviors, which are typical of multimedia applications. The few studies that consider dynamic mapping of applications do not consider throughput degradation, which directly impacts user satisfaction. This article proposes a runtime technique to analyze the execution trace of an application modeled as Synchronous Data Flow Graphs (SDFGs) to determine its mapping on a multiprocessor system with heterogeneous processing units for different fault scenarios. Further, communication energy is minimized for each of these mappings while satisfying the throughput constraint. Experiments conducted with synthetic and real SDFGs demonstrate that the proposed technique achieves significant improvement with respect to the state-of-the-art approaches in terms of throughput and storage overhead with less than 20% energy overhead.

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More information

Published date: May 2015
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 376917
URI: https://eprints.soton.ac.uk/id/eprint/376917
ISSN: 1936-7406
PURE UUID: f3f0649c-bc87-45df-96ae-f2cfe113eeae

Catalogue record

Date deposited: 13 May 2015 10:59
Last modified: 17 Jul 2017 21:05

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