Climate change poses one of the most significant challenges to humanity. As a
result of these climatic changes, the frequency of weather, climate, and
water-related disasters has multiplied fivefold over the past 50 years,
resulting in over 2 million deaths and losses exceeding 3.64trillionUSD.LeveragingAI−poweredtechnologiesforsustainabledevelopmentandcombatingclimatechangeisapromisingavenue.NumeroussignificantpublicationsarededicatedtousingAItoimproverenewableenergyforecasting,enhancewastemanagement,andmonitorenvironmentalchangesinrealtime.However,veryfewresearchstudiesfocusonmakingAIitselfenvironmentallysustainable.ThisoversightregardingthesustainabilityofAIwithinthefieldmightbeattributedtoamindsetgapandtheabsenceofcomprehensiveenergydatasets.Inaddition,withtheubiquityofedgeAIsystemsandapplications,especiallyon−devicelearning,thereisapressingneedtomeasure,analyze,andoptimizetheirenvironmentalsustainability,suchasenergyefficiency.Tothisend,inthispaper,weproposelarge−scaleenergydatasetsforedgeAI,namedDeepEn2023,coveringawiderangeofkernels,state−of−the−artdeepneuralnetworkmodels,andpopularedgeAIapplications.WeanticipatethatDeepEn2023willimprovetransparencyinsustainabilityinon−devicedeeplearningacrossarangeofedgeAIsystemsandapplications.Formoreinformation,includingaccesstothedatasetandcode,pleasevisithttps://amai−gsu.github.io/DeepEn2023.
