We　propose　BitFlow-Net,　a　method　to　simplify　binary　CNN　model　to　almost　no　floating-point　multiplication　at　inference　time.　Recently,　a　lot　of　variations　of　binarized　networks　were　proposed　trying　to　achieve　high　accuracy　while　replacing　resource-consuming　floating-point　multiplication　with　bit　operation.　These　methods　usually　require　scaling　factor　and　BatchNorm　to　achieve　comparable　accuracy　as　their　full-precision　counterparts.　However,　data　flow　have　to　be　frequently　converted　between　floating-point　data　and　bit　data　due　to　the　multiplication　with　scaling　factor　and　in　BatchNorm.　Such　conversion　will　cost　extra　resources　and　time　when　implemented　on　edge　hardware.　Motivated　by　that,　we　further　explore　and　reveal　some　basic　attributes　of　BNN　based　on　previous　works　and　propose　a　new　method　to　simplify　binary　network.　As　a　result,　our　model　could　inference　with　most　of　its　data　flow　remains　bit　flow.　Such　a　network　architecture　will　greatly　reduce　the　design　complexity　when　implemented　on　ASIC　or　FPGA.　Our　method　performs　no　accuracy　degradation　on　ImageNet　compared　to　state-of-the-art　BNN　models　but　without　extra　floating-point　multiplications.　©　2018　IEEE.