Nowadays,　the　most　heterogeneous　architectures　were　made　up　by　the　various　IP　modules　of　different　hardware　vendors,　but　this　model　is　less　efficiently.　In　order　to　solve　this　problem,　AMD　joint　other　hardware　vendors　proposed　heterogeneous　system　architecture　(HSA)　specification.　On　the　one　hand,　the　HSA　could　help　developers　to　accelerate　the　design　process　and　programming.　On　the　other　hand,　it　improved　the　system　performance　and　reduced　the　power.　In　this　paper　we　presented　the　implementation　of　a　framework　for　accelerating　training　and　classification　of　arbitrary　Convolutional　Neural　Networks　(CNNs)　on　the　HSA,　on　the　basis　of　implementation,　we　presented　tow　accelerated　methods　that　are　Online　update　weights　and　letting　CPU　to　participate　in　calculation.　Experimental　results　showed　that　the　implementation　of　CNNs　on　HSA　4　to　10　times　faster　than　on　the　CPU.