Show simple item record

dc.contributor.author
Giardino, Michael Joseph
dc.contributor.author
Schwyn, Daniel
dc.contributor.author
Ferri, Bonnie
dc.contributor.author
Ferri, Aldo
dc.date.accessioned
2022-05-16T07:58:24Z
dc.date.available
2022-05-14T03:46:01Z
dc.date.available
2022-05-16T07:58:24Z
dc.date.issued
2021
dc.identifier.isbn
978-1-6654-3860-5
en_US
dc.identifier.isbn
978-1-7281-8752-5
en_US
dc.identifier.other
10.1109/MCSoC51149.2021.00040
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/547178
dc.description.abstract
This paper describes the design and performance of Q-learning-based quality-of-service manager (2QoSM) for compute-aware applications (CAAs) as part of platform-agnostic resource management framework. CAAs and hardware are able to share metrics of performance with the 2QoSM and the 2QoSM can attempt to reconfigure CAAs and hardware to meet performance targets. This enables many co-design benefits while allowing for policy and platform portability. The use of QLearning allows online generation of the power management policy without requiring details about system state or actions, and can meet different goals including error, power minimization, or a combination of both. 2QoSM, evaluated using an embedded MCSoC controlling a mobile robot, reduces power compared to the Linux on-demand governor by 38.7-42.6% and a situation-aware governor by 4.0-10.2%. An error-minimization policy obtained a reduction in path-following error of 4.6-8.9%.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
Power aware computing
en_US
dc.subject
Q learning
en_US
dc.subject
Autonomous robot
en_US
dc.subject
Middleware
en_US
dc.subject
Application guidance
en_US
dc.subject
Machine learning
en_US
dc.title
2QoSM: A Q-Learner QoS Manager for Application-Guided Power-Aware Systems
en_US
dc.type
Conference Paper
dc.date.published
2022-02-04
ethz.book.title
2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)
en_US
ethz.pages.start
218
en_US
ethz.pages.end
225
en_US
ethz.event
14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC 2021)
en_US
ethz.event.location
Singapore
en_US
ethz.event.date
December 20-23, 2021
en_US
ethz.identifier.wos
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2022-05-14T03:46:11Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2022-05-16T07:58:37Z
ethz.rosetta.lastUpdated
2022-05-16T07:58:37Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=2QoSM:%20A%20Q-Learner%20QoS%20Manager%20for%20Application-Guided%20Power-Aware%20Systems&rft.date=2021&rft.spage=218&rft.epage=225&rft.au=Giardino,%20Michael%20Joseph&Schwyn,%20Daniel&Ferri,%20Bonnie&Ferri,%20Aldo&rft.isbn=978-1-6654-3860-5&978-1-7281-8752-5&rft.genre=proceeding&rft_id=info:doi/10.1109/MCSoC51149.2021.00040&rft.btitle=2021%20IEEE%2014th%20International%20Symposium%20on%20Embedded%20Multicore/Many-core%20Systems-on-Chip%20(MCSoC)
 Search print copy at ETH Library

Files in this item

FilesSizeFormatOpen in viewer

There are no files associated with this item.

Publication type

Show simple item record