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Run-Time Scenario-Based MPSoC Mapping Reconfiguration Using Machine Learning Models

Spieck J, Wildermann S, Teich J (2020)

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Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Conference Proceedings Title: Post-workshop proceedings of 2019 ACM/IEEE 1st Workshop on Machine Learning for CAD

Event location: Canmore, Alberta, Canada

DOI: 10.1109/MLCAD48534.2019.9142060

Abstract

Applications with highly input-dependent workload

and execution behavior cannot be optimally executed by a single

mapping of application tasks to a heterogeneous multi-core target

architecture. Albeit mapping a task to a resource with high

computational power may be suitable for input triggering a

high workload of this task, it may be more efficient to map

another task to the resource in case of input providing low

workload for the former and high workload for the latter. As

a remedy, we propose to group inputs evoking similar workload

and execution characteristics into so-called workload scenarios for

which specialized mappings targeted at the common workload

distribution in the scenario are provided. Optimized mappings

for each scenario can be determined by a scenario-based design

space exploration at design time.

At run time, applications process a stream of input data whose

scenario affiliation is a priori unknown. This entails two coupled

tasks: First, we have to identify the scenario of the current input

data based on its execution characteristics. Second, we have to

choose an application mapping for processing the current input

prior to its execution on the basis of the concluded scenarios

of the past input and the currently active scenario-associated

mapping. Note that switching between scenarios may come at

a non-negligible reconfiguration cost that could decrease the

advantage gained by a more suitable mapping.

Both tasks are tackled by a proposed run-time reconfiguration

manager, which is built on machine learning models. These

models learn a strategy for identifying scenarios and selecting

adequate mappings by design-time training. Here, different

machine learning models are compared for their applicability.

An evaluation of the run-time manager based on a ray tracing

and stitching application shows significant latency improvements

compared to an approach with a single mapping optimized for

the average-case input.

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How to cite

APA:

Spieck, J., Wildermann, S., & Teich, J. (2020). Run-Time Scenario-Based MPSoC Mapping Reconfiguration Using Machine Learning Models. In Post-workshop proceedings of 2019 ACM/IEEE 1st Workshop on Machine Learning for CAD. Canmore, Alberta, Canada, CA.

MLA:

Spieck, Jan, Stefan Wildermann, and Jürgen Teich. "Run-Time Scenario-Based MPSoC Mapping Reconfiguration Using Machine Learning Models." Proceedings of the 1st ACM/IEEE Workshop on Machine Learning for CAD (MLCAD), Canmore, Alberta, Canada 2020.

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