{
  "authors": [
    {
      "affiliation": "University of Washington, USA", 
      "name": "8ebae5a1164dc291"
    }, 
    {
      "affiliation": "University of Toronto, Canada", 
      "name": "6b499e9486a5536b"
    }, 
    {
      "affiliation": "EPFL, Switzerland", 
      "name": "d1488e8c3a674fa8"
    }, 
    {
      "affiliation": "cTuning foundation, France / dividiti, UK", 
      "name": "0728a400aa1c86fe"
    }, 
    {
      "affiliation": "dividiti, UK", 
      "name": "07b8b4bd98945c99"
    }, 
    {
      "affiliation": "University of Washington, USA", 
      "name": "5a7e00eedecd3a91"
    }, 
    {
      "affiliation": "Cornell University, USA", 
      "name": "1cf026249ecae5b2"
    }, 
    {
      "affiliation": "University of Cambridge, UK", 
      "name": "07f1639572f4e789"
    }
  ], 
  "colocated": "ACM ASPLOS 2018", 
  "notes": [
    {
      "italic": "yes", 
      "note": "Sponsored by ACM, cTuning foundation and dividiti"
    }, 
    {
      "italic": "yes", 
      "note": "Uses Collective Knowledge Framework to share resulting workflows and artifacts in a common format for a unified and unbiased benchmarking and to let the community reuse and build upon best found solutions"
    }, 
    {
      "italic": "yes", 
      "note": "Supports our new publication model in computer engineering where all research artifacts (tools, benchmarks, datasets, models) are continuously shared and validated by the community [$#cm_29db2248aba45e59_40a4b58adfb594a8#$,$#cm_77154d189d2e226c_7dd14d78bfdb9af2#$,$#cm_77154d189d2e226c_9aad4281e1c4e42a#$,$#cm_77154d189d2e226c_cc51a9904ca8ad12#$,$#cm_77154d189d2e226c_b48be4e687d9401f#$,$#cm_77154d189d2e226c_be9926d412125773#$]"
    }, 
    {
      "italic": "yes", 
      "note": "Related publications [$#cm_29db2248aba45e59_0c7348dfbadd5b95#$, $#cm_29db2248aba45e59_c4b24bff57f4ad07#$, $#cm_29db2248aba45e59_cd11e3a188574d80#$, $#cm_29db2248aba45e59_9671da4c2f971915#$, $#cm_29db2248aba45e59_6f40bc99c4f7df58#$, $#cm_29db2248aba45e59_0c44d9a2db3de3c9#$]"
    }
  ], 
  "place": "Williamsburg, VA, USA", 
  "start_iso_date": "2018-03-24", 
  "title": "ReQuEST: 1st open, reproducible and Pareto efficient SW/HW co-design competition for deep learning (speed, accuracy, costs)", 
  "type": {
    "name": "competition"
  }, 
  "urls": [
    {
      "title": "Website", 
      "url": "http://cKnowledge.org/request"
    }, 
    {
      "title": "ACM proceedings", 
      "url": "https://doi.org/10.1145/3229762"
    }, 
    {
      "title": "Online results report (proceedings front matter)", 
      "url": "https://portalparts.acm.org/3230000/3229762/fm/frontmatter.pdf"
    }, 
    {
      "title": "All reusable CK workflows and components", 
      "url": "https://github.com/ctuning/ck-request-asplos18-results"
    }, 
    {
      "title": "Reddit discussion", 
      "url": "https://www.reddit.com/r/MachineLearning/comments/7hgrnw/n_1st_open_tournament_on_pareto_efficient_deep/"
    }, 
    {
      "title": "Collective Knowledge Framework used to put systems and ML researchers on the same page and share AI artifacts as customized, reproducible, reusable and optimized components with a unified JSON API", 
      "url": "http://cKnowledge.org"
    }, 
    {
      "title": "Live ReQuEST scoreboard with reproducible results", 
      "url": "http://cKnowledge.org/requst-results"
    }
  ], 
  "when": "March 2018"
}

{}