Convolutional　Neural　Networks　(CNNs)　have　shown　a　great　potential　in　different　application　domains　including　object　detection,　image　classification,　natural　language　processing,　and　speech　recognition.　Since　the　depth　of　the　neural　network　architectures　keep　growing　and　the　requirement　of　the　large-scale　dataset,　to　design　a　high-performance　computing　hardware　for　training　CNNs　is　very　necessary.　In　this　paper,　we　measure　the　performance　of　different　configuration　on　GPU　platform　and　learning　the　patterns　through　training　two　CNNs　architectures,　LeNet　and　MiniNet,　both　perform　the　image　classification.　Observe　the　results　of　measurements,　we　indicate　the　correlation　between　LAD　cache　and　the　performance　of　GPUs　during　the　training　process.　Also,　we　demonstrate　that　L2D　cache　slightly　influences　the　performance.　The　network　traffic　intensity　with　both　CNN　models　shows　that　each　layer　has　distinct　patterns　of　traffic　intensity.