Since　2006,　there　have　been　significant　advances　in　deep　learning　algorithms,　and　they　have　shown　superior　performance　in　audio　and　image　processing.　In　this　paper,　we　explore　the　feasibility　of　applying　deep　learning　algorithms　to　branch　prediction.　We　treat　branch　prediction　as　a　classification　problem　and　compare　the　effectiveness　of　deep　learning　with　existing　branch　predictors.　We　make　several　interesting　observations　from　our　study.　The　first　is　that　for　branch　prediction,　the　deep　learning　algorithm　based　on　deep　belief　networks　outperforms　the　prior　work,　but　only　outperforms　state-of-the-art　branch　predictors,　such　as　the　TAgged　GEometric　length　(TAGE)　predictors,　for　several　benchmarks.　Compared　with　the　much　simpler　perceptron　branch　classifier,　the　deep　learning　classifier　reduces　the　average　misprediction　rate　by　3%-4%　for　the　benchmarks　in　this　paper.　Second,　we　analyze　the　impact　of　the　length　of　hashed　program　counter,　local　history　register,　global　history　register,　and　branch　global　addresses　of　deep　learning　classifiers　on　the　misprediction　rate.　Our　results　show　that　an　adaptive　length　of　the　history　information　is　a　better　choice　than　the　longest　history.　Third,　compared　with　TAGE,　the　hardware　budget　of　our　model　is　less　than　1%　of　the　TAGE　predictor.