Recently,　there　have　been　significant　advances　in　deep　neural　networks　(DNNs)　and　they　have　shown　distinctive　performance　in　speech　recognition,　natural　language　processing,　and　image　recognition.　In　this　paper,　we　explore　DNNs　to　push　the　limit　for　branch　prediction.　We　treat　branch　prediction　as　a　classification　problem　and　employ　both　deep　convolutional　neural　networks　(CNNs),　ranging　from　LeNet　to　ResNet-50,　and　deep　belief　network　(DBN)　for　branch　prediction.　We　compare　the　effectiveness　of　DNNs　with　the　state-of-the-art　branch　predictors,　including　the　perceptron,　our　prior　work,　Multi-poTAGE+SC,　and　MTAGE+SC　branch　predictors.　The　last　two　are　the　most　recent　winners　of　championship　branch　prediction　(CBP)　contests.　Several　interesting　observations　emerged　from　our　study.　First,　for　branch　prediction,　the　DNNs　outperform　the　perceptron　model　as　high　as　60-80%.　Second,　we　analyze　the　impact　of　the　depth　of　CNNs　(i.e.,　number　of　convolutional　layers　and　pooling　layers)　on　the　misprediction　rates.　The　results　confirm　that　deeper　CNN　structures　can　lead　to　lower　misprediction　rates.　Third,　the　DBN　could　outperform　our　prior　work,　but　not　outperform　the　state-of-the-art　TAGE-like　branch　predictor;　the　ResNet-50　could　not　only　outperform　our　prior　work,　but　also　the　Multi-poTAGE+SC　and　MTAGE+SC.　©　2013　IEEE.