Multiple　CPUs　and　GPUs　are　integrated　on　the　same　chip　to　share　memory,　and　access　requests　between　cores　are　interfering　with　each　other.　Memory　requests　from　the　GPU　seriously　interfere　with　the　CPU　memory　access　performance.　Requests　between　multiple　CPUs　are　intertwined　when　accessing　memory,　and　its　performance　is　greatly　affected.　The　difference　in　access　latency　between　GPU　cores　increases　the　average　latency　of　memory　accesses.　In　order　to　solve　the　problems　encountered　in　the　shared　memory　of　heterogeneous　multi-core　systems,　we　propose　a　step-by-step　memory　scheduling　strategy,　which　improve　the　system　performance.　The　step-by-step　memory　scheduling　strategy　first　creates　a　new　memory　request　queue　based　on　the　request　source　and　isolates　the　CPU　requests　from　the　GPU　requests　when　the　memory　controller　receives　the　memory　request,　thereby　preventing　the　GPU　request　from　interfering　with　the　CPU　request.　Then,　for　the　CPU　request　queue,　a　dynamic　bank　partitioning　strategy　is　implemented,　which　dynamically　maps　it　to　different　bank　sets　according　to　different　memory　characteristics　of　the　application,　and　eliminates　memory　request　interference　of　multiple　CPU　applications　without　affecting　bank-level　parallelism.　Finally,　for　the　GPU　request　queue,　the　criticality　is　introduced　to　measure　the　difference　of　the　memory　access　latency　between　the　cores.　Based　on　the　first　ready-first　come　first　served　strategy,　we　implemented　criticality-aware　memory　scheduling　to　balance　the　locality　and　criticality　of　application　access.