With　the　pursuit　of　higher　accuracy,　the　convolutional　neural　network　has　become　deeper.　Thus,　the　hardware　overhead　occupied　have　increased.　Batch　Normalization　(BN)　operation　is　an　indispensable　part　of　the　network,　and　its　occupied　hardware　resources　cannot　be　ignored.　In　this　paper,　aiming　to　reduce　hardware　overhead　caused　by　BN　operation,　we　combine　BN　operation　and　ReLU　activation　function　and　propose　the　BNReLU　operation　without　retraining　the　network.　The　experiment　results　show　that　compared　with　traditional　BN+ReLU　layer,　hardware　overhead　of　BNReLU　operation　is　reduced　by　about　50%.　With　32-bit　float　point　input　for　each　layer,　the　hardware　overhead　of　BRAM,　DSP,　FF,　and　LUT　for　convolutional　layer　in　the　FPGA　is　reduced　by　17.16%,　23.08%,　17.64%,　and　16.05%.　©　2019　IEEE.