Power Supply Noise Aware Task Scheduling on Homogeneous 3D MPSoCs Considering the Thermal Constraint View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-09

AUTHORS

Ying-Lin Zhao, Jian-Lei Yang, Wei-Sheng Zhao, Aida Todri-Sanial, Yuan-Qing Cheng

ABSTRACT

Thanks to the emerging 3D integration technology, The multiprocessor system on chips (MPSoCs) can now integrate more IP cores on chip with improved energy efficiency. However, several severe challenges also rise up for 3D ICs due to the die-stacking architecture. Among them, power supply noise becomes a big concern. In the paper, we investigate power supply noise (PSN) interactions among different cores and tiers and show that PSN variations largely depend on task assignments. On the other hand, high integration density incurs a severe thermal issue on 3D ICs. In the paper, we propose a novel task scheduling framework considering both the PSN and the thermal issue. It mainly consists of three parts. First, we extract current stimuli of running tasks by analyzing their power traces derived from architecture level simulations. Second, we develop an efficient power delivery network (PDN) solver to evaluate PSN magnitudes efficiently. Third, we propose a heuristic algorithm to solve the formulated task scheduling problem. Compared with the state-of-the-art task assignment algorithm, the proposed method can reduce PSN by 12% on a 2 × 2 × 2 3D MPSoCs and by 14% on a 3 × 3 × 3 3D MPSoCs. The end-to-end task execution time also improves as much as 5.5% and 7.8% respectively due to the suppressed PSN. More... »

PAGES

966-983

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11390-018-1868-6

DOI

http://dx.doi.org/10.1007/s11390-018-1868-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106967382


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0906", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Electrical and Electronic Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Beihang University", 
          "id": "https://www.grid.ac/institutes/grid.64939.31", 
          "name": [
            "Fert Beijing Research Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing, 100191, Beijing, China", 
            "School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China", 
            "Qingdao Research Institute, Beihang University, 266041, Qingdao, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhao", 
        "givenName": "Ying-Lin", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Beihang University", 
          "id": "https://www.grid.ac/institutes/grid.64939.31", 
          "name": [
            "Fert Beijing Research Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing, 100191, Beijing, China", 
            "School of Computer Science and Engineering, Beihang University, 100191, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Jian-Lei", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Beihang University", 
          "id": "https://www.grid.ac/institutes/grid.64939.31", 
          "name": [
            "Fert Beijing Research Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing, 100191, Beijing, China", 
            "School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China", 
            "Qingdao Research Institute, Beihang University, 266041, Qingdao, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhao", 
        "givenName": "Wei-Sheng", 
        "id": "sg:person.016636512717.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016636512717.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "French National Centre for Scientific Research", 
          "id": "https://www.grid.ac/institutes/grid.4444.0", 
          "name": [
            "Laboratory of Informatics, Robotics and Microelectronics, University of Montpellier, 34095, Montpellier, France", 
            "National Center for Scientific Research, 34095, Montpellier, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Todri-Sanial", 
        "givenName": "Aida", 
        "id": "sg:person.015540434373.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015540434373.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Beihang University", 
          "id": "https://www.grid.ac/institutes/grid.64939.31", 
          "name": [
            "School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China", 
            "Qingdao Research Institute, Beihang University, 266041, Qingdao, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cheng", 
        "givenName": "Yuan-Qing", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1145/2429384.2429434", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002296461"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1283780.1283826", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007038877"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/288548.288617", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015960649"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/378239.379023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018003132"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1146909.1146980", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028553091"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1391469.1391657", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036698243"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1941487.1941507", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039221534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1289816.1289846", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043757903"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/337292.337359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048439835"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/871506.871529", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049300165"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/635506.605403", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050862724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1049/mnl.2012.0598", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056885145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mdt.2007.79", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061399760"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tadvp.2004.825480", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061480600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcad.2005.844106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061537164"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcapt.2005.859737", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061539794"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpds.2009.27", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061753488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvlsi.2006.876103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061815416"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvlsi.2009.2038165", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061816243"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvlsi.2010.2055907", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061816358"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvlsi.2010.2058873", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061816367"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvlsi.2011.2167359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061816630"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvlsi.2011.2182067", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061816688"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvlsi.2012.2187081", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061816704"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvlsi.2016.2549275", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061817828"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1150019.1136497", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063152126"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1147/rd.461.0005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063182630"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/9789812792228_0005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088736871"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/isqed.2010.5450550", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093203320"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/aspdac.2014.6742948", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093338014"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iitc.2010.5510728", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093423841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ectc.2007.374017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093474661"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/hpca.2002.995694", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093609268"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ectc.2010.5490753", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093621831"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/epep.2007.4387161", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093877218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/micro.2004.35", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094326192"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/hpca.2003.1183526", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094503445"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/date.2012.6176613", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094606633"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/isqed.2010.5450497", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094680430"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/isca.2014.6853199", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094909839"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/dac.1997.597223", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094927486"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccad.2008.4681594", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095007278"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ectc.2012.6248906", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095026338"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccad.2011.6105382", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095304249"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/date.2007.364663", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095396084"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccad.2008.4681641", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095575554"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/isscc.2010.5434077", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095662195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/date.2009.5090632", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1096328037"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/date.2011.5763053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1097403999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/date.2011.5763237", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1097407053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/date.2011.5763237", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1097407053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.7873/date.2015.0724", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099486233"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-09", 
    "datePublishedReg": "2018-09-01", 
    "description": "Thanks to the emerging 3D integration technology, The multiprocessor system on chips (MPSoCs) can now integrate more IP cores on chip with improved energy efficiency. However, several severe challenges also rise up for 3D ICs due to the die-stacking architecture. Among them, power supply noise becomes a big concern. In the paper, we investigate power supply noise (PSN) interactions among different cores and tiers and show that PSN variations largely depend on task assignments. On the other hand, high integration density incurs a severe thermal issue on 3D ICs. In the paper, we propose a novel task scheduling framework considering both the PSN and the thermal issue. It mainly consists of three parts. First, we extract current stimuli of running tasks by analyzing their power traces derived from architecture level simulations. Second, we develop an efficient power delivery network (PDN) solver to evaluate PSN magnitudes efficiently. Third, we propose a heuristic algorithm to solve the formulated task scheduling problem. Compared with the state-of-the-art task assignment algorithm, the proposed method can reduce PSN by 12% on a 2 \u00d7 2 \u00d7 2 3D MPSoCs and by 14% on a 3 \u00d7 3 \u00d7 3 3D MPSoCs. The end-to-end task execution time also improves as much as 5.5% and 7.8% respectively due to the suppressed PSN.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11390-018-1868-6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1320078", 
        "issn": [
          "1666-6046", 
          "1666-6038"
        ], 
        "name": "Journal of Computer Science and Technology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "33"
      }
    ], 
    "name": "Power Supply Noise Aware Task Scheduling on Homogeneous 3D MPSoCs Considering the Thermal Constraint", 
    "pagination": "966-983", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "0a0b2dafb627d328da4ef3f7bfb4a3522877e092350cd5890db3f3b9c5fab706"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11390-018-1868-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1106967382"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11390-018-1868-6", 
      "https://app.dimensions.ai/details/publication/pub.1106967382"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T15:04", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8663_00000526.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11390-018-1868-6"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11390-018-1868-6'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11390-018-1868-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11390-018-1868-6'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11390-018-1868-6'


 

This table displays all metadata directly associated to this object as RDF triples.

246 TRIPLES      21 PREDICATES      78 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11390-018-1868-6 schema:about anzsrc-for:09
2 anzsrc-for:0906
3 schema:author N9f358cd7fc2e4ae281ce2eb4f3b97010
4 schema:citation https://doi.org/10.1049/mnl.2012.0598
5 https://doi.org/10.1109/aspdac.2014.6742948
6 https://doi.org/10.1109/dac.1997.597223
7 https://doi.org/10.1109/date.2007.364663
8 https://doi.org/10.1109/date.2009.5090632
9 https://doi.org/10.1109/date.2011.5763053
10 https://doi.org/10.1109/date.2011.5763237
11 https://doi.org/10.1109/date.2012.6176613
12 https://doi.org/10.1109/ectc.2007.374017
13 https://doi.org/10.1109/ectc.2010.5490753
14 https://doi.org/10.1109/ectc.2012.6248906
15 https://doi.org/10.1109/epep.2007.4387161
16 https://doi.org/10.1109/hpca.2002.995694
17 https://doi.org/10.1109/hpca.2003.1183526
18 https://doi.org/10.1109/iccad.2008.4681594
19 https://doi.org/10.1109/iccad.2008.4681641
20 https://doi.org/10.1109/iccad.2011.6105382
21 https://doi.org/10.1109/iitc.2010.5510728
22 https://doi.org/10.1109/isca.2014.6853199
23 https://doi.org/10.1109/isqed.2010.5450497
24 https://doi.org/10.1109/isqed.2010.5450550
25 https://doi.org/10.1109/isscc.2010.5434077
26 https://doi.org/10.1109/mdt.2007.79
27 https://doi.org/10.1109/micro.2004.35
28 https://doi.org/10.1109/tadvp.2004.825480
29 https://doi.org/10.1109/tcad.2005.844106
30 https://doi.org/10.1109/tcapt.2005.859737
31 https://doi.org/10.1109/tpds.2009.27
32 https://doi.org/10.1109/tvlsi.2006.876103
33 https://doi.org/10.1109/tvlsi.2009.2038165
34 https://doi.org/10.1109/tvlsi.2010.2055907
35 https://doi.org/10.1109/tvlsi.2010.2058873
36 https://doi.org/10.1109/tvlsi.2011.2167359
37 https://doi.org/10.1109/tvlsi.2011.2182067
38 https://doi.org/10.1109/tvlsi.2012.2187081
39 https://doi.org/10.1109/tvlsi.2016.2549275
40 https://doi.org/10.1142/9789812792228_0005
41 https://doi.org/10.1145/1146909.1146980
42 https://doi.org/10.1145/1150019.1136497
43 https://doi.org/10.1145/1283780.1283826
44 https://doi.org/10.1145/1289816.1289846
45 https://doi.org/10.1145/1391469.1391657
46 https://doi.org/10.1145/1941487.1941507
47 https://doi.org/10.1145/2429384.2429434
48 https://doi.org/10.1145/288548.288617
49 https://doi.org/10.1145/337292.337359
50 https://doi.org/10.1145/378239.379023
51 https://doi.org/10.1145/635506.605403
52 https://doi.org/10.1145/871506.871529
53 https://doi.org/10.1147/rd.461.0005
54 https://doi.org/10.7873/date.2015.0724
55 schema:datePublished 2018-09
56 schema:datePublishedReg 2018-09-01
57 schema:description Thanks to the emerging 3D integration technology, The multiprocessor system on chips (MPSoCs) can now integrate more IP cores on chip with improved energy efficiency. However, several severe challenges also rise up for 3D ICs due to the die-stacking architecture. Among them, power supply noise becomes a big concern. In the paper, we investigate power supply noise (PSN) interactions among different cores and tiers and show that PSN variations largely depend on task assignments. On the other hand, high integration density incurs a severe thermal issue on 3D ICs. In the paper, we propose a novel task scheduling framework considering both the PSN and the thermal issue. It mainly consists of three parts. First, we extract current stimuli of running tasks by analyzing their power traces derived from architecture level simulations. Second, we develop an efficient power delivery network (PDN) solver to evaluate PSN magnitudes efficiently. Third, we propose a heuristic algorithm to solve the formulated task scheduling problem. Compared with the state-of-the-art task assignment algorithm, the proposed method can reduce PSN by 12% on a 2 × 2 × 2 3D MPSoCs and by 14% on a 3 × 3 × 3 3D MPSoCs. The end-to-end task execution time also improves as much as 5.5% and 7.8% respectively due to the suppressed PSN.
58 schema:genre research_article
59 schema:inLanguage en
60 schema:isAccessibleForFree false
61 schema:isPartOf Nb4833446b58e4188836d846e2ef389dd
62 Nbc870123363340f997c6789e6635b475
63 sg:journal.1320078
64 schema:name Power Supply Noise Aware Task Scheduling on Homogeneous 3D MPSoCs Considering the Thermal Constraint
65 schema:pagination 966-983
66 schema:productId N07b41804a46f4b249d5483539e46e34b
67 N45aa2b5771ea4a9e832b3fc7530d1778
68 Nb01cf6b8cd4e41f8900c033732637809
69 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106967382
70 https://doi.org/10.1007/s11390-018-1868-6
71 schema:sdDatePublished 2019-04-10T15:04
72 schema:sdLicense https://scigraph.springernature.com/explorer/license/
73 schema:sdPublisher N219dc51be2f64f9bbd6843455c177c17
74 schema:url http://link.springer.com/10.1007%2Fs11390-018-1868-6
75 sgo:license sg:explorer/license/
76 sgo:sdDataset articles
77 rdf:type schema:ScholarlyArticle
78 N07b41804a46f4b249d5483539e46e34b schema:name dimensions_id
79 schema:value pub.1106967382
80 rdf:type schema:PropertyValue
81 N219dc51be2f64f9bbd6843455c177c17 schema:name Springer Nature - SN SciGraph project
82 rdf:type schema:Organization
83 N3d7f1d0775b94170b8048d1ef417766f schema:affiliation https://www.grid.ac/institutes/grid.64939.31
84 schema:familyName Zhao
85 schema:givenName Ying-Lin
86 rdf:type schema:Person
87 N45aa2b5771ea4a9e832b3fc7530d1778 schema:name readcube_id
88 schema:value 0a0b2dafb627d328da4ef3f7bfb4a3522877e092350cd5890db3f3b9c5fab706
89 rdf:type schema:PropertyValue
90 N5601e66366aa4a0dbcd4381d7a939cab schema:affiliation https://www.grid.ac/institutes/grid.64939.31
91 schema:familyName Yang
92 schema:givenName Jian-Lei
93 rdf:type schema:Person
94 N62ea1315abd0482aaeae3103c469fdb8 rdf:first Nd91b2ddbc4f848cca5e8a60d10040ca6
95 rdf:rest rdf:nil
96 N70174082bca84a39ba0cdce8bc2cf3d6 rdf:first sg:person.016636512717.27
97 rdf:rest Ndc4fa52940f84527b9268ab2d1954e57
98 N9f358cd7fc2e4ae281ce2eb4f3b97010 rdf:first N3d7f1d0775b94170b8048d1ef417766f
99 rdf:rest Nbfa3a713d1c64c918904c5c1b4f76865
100 Nb01cf6b8cd4e41f8900c033732637809 schema:name doi
101 schema:value 10.1007/s11390-018-1868-6
102 rdf:type schema:PropertyValue
103 Nb4833446b58e4188836d846e2ef389dd schema:volumeNumber 33
104 rdf:type schema:PublicationVolume
105 Nbc870123363340f997c6789e6635b475 schema:issueNumber 5
106 rdf:type schema:PublicationIssue
107 Nbfa3a713d1c64c918904c5c1b4f76865 rdf:first N5601e66366aa4a0dbcd4381d7a939cab
108 rdf:rest N70174082bca84a39ba0cdce8bc2cf3d6
109 Nd91b2ddbc4f848cca5e8a60d10040ca6 schema:affiliation https://www.grid.ac/institutes/grid.64939.31
110 schema:familyName Cheng
111 schema:givenName Yuan-Qing
112 rdf:type schema:Person
113 Ndc4fa52940f84527b9268ab2d1954e57 rdf:first sg:person.015540434373.92
114 rdf:rest N62ea1315abd0482aaeae3103c469fdb8
115 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
116 schema:name Engineering
117 rdf:type schema:DefinedTerm
118 anzsrc-for:0906 schema:inDefinedTermSet anzsrc-for:
119 schema:name Electrical and Electronic Engineering
120 rdf:type schema:DefinedTerm
121 sg:journal.1320078 schema:issn 1666-6038
122 1666-6046
123 schema:name Journal of Computer Science and Technology
124 rdf:type schema:Periodical
125 sg:person.015540434373.92 schema:affiliation https://www.grid.ac/institutes/grid.4444.0
126 schema:familyName Todri-Sanial
127 schema:givenName Aida
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015540434373.92
129 rdf:type schema:Person
130 sg:person.016636512717.27 schema:affiliation https://www.grid.ac/institutes/grid.64939.31
131 schema:familyName Zhao
132 schema:givenName Wei-Sheng
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016636512717.27
134 rdf:type schema:Person
135 https://doi.org/10.1049/mnl.2012.0598 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056885145
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1109/aspdac.2014.6742948 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093338014
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/dac.1997.597223 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094927486
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1109/date.2007.364663 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095396084
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1109/date.2009.5090632 schema:sameAs https://app.dimensions.ai/details/publication/pub.1096328037
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1109/date.2011.5763053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1097403999
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1109/date.2011.5763237 schema:sameAs https://app.dimensions.ai/details/publication/pub.1097407053
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1109/date.2012.6176613 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094606633
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1109/ectc.2007.374017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093474661
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1109/ectc.2010.5490753 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093621831
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1109/ectc.2012.6248906 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095026338
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1109/epep.2007.4387161 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093877218
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1109/hpca.2002.995694 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093609268
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1109/hpca.2003.1183526 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094503445
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1109/iccad.2008.4681594 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095007278
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1109/iccad.2008.4681641 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095575554
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1109/iccad.2011.6105382 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095304249
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1109/iitc.2010.5510728 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093423841
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1109/isca.2014.6853199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094909839
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1109/isqed.2010.5450497 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094680430
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1109/isqed.2010.5450550 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093203320
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1109/isscc.2010.5434077 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095662195
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1109/mdt.2007.79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061399760
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1109/micro.2004.35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094326192
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1109/tadvp.2004.825480 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061480600
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1109/tcad.2005.844106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061537164
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1109/tcapt.2005.859737 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061539794
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1109/tpds.2009.27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061753488
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1109/tvlsi.2006.876103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061815416
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1109/tvlsi.2009.2038165 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061816243
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1109/tvlsi.2010.2055907 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061816358
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1109/tvlsi.2010.2058873 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061816367
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1109/tvlsi.2011.2167359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061816630
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1109/tvlsi.2011.2182067 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061816688
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1109/tvlsi.2012.2187081 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061816704
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1109/tvlsi.2016.2549275 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061817828
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1142/9789812792228_0005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088736871
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1145/1146909.1146980 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028553091
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1145/1150019.1136497 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063152126
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1145/1283780.1283826 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007038877
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1145/1289816.1289846 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043757903
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1145/1391469.1391657 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036698243
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1145/1941487.1941507 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039221534
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1145/2429384.2429434 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002296461
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1145/288548.288617 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015960649
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1145/337292.337359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048439835
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1145/378239.379023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018003132
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1145/635506.605403 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050862724
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1145/871506.871529 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049300165
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1147/rd.461.0005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063182630
234 rdf:type schema:CreativeWork
235 https://doi.org/10.7873/date.2015.0724 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099486233
236 rdf:type schema:CreativeWork
237 https://www.grid.ac/institutes/grid.4444.0 schema:alternateName French National Centre for Scientific Research
238 schema:name Laboratory of Informatics, Robotics and Microelectronics, University of Montpellier, 34095, Montpellier, France
239 National Center for Scientific Research, 34095, Montpellier, France
240 rdf:type schema:Organization
241 https://www.grid.ac/institutes/grid.64939.31 schema:alternateName Beihang University
242 schema:name Fert Beijing Research Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing, 100191, Beijing, China
243 Qingdao Research Institute, Beihang University, 266041, Qingdao, China
244 School of Computer Science and Engineering, Beihang University, 100191, Beijing, China
245 School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China
246 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...