When　multiple　central　processing　unit　(CPU)　cores　and　integrated　graphics　processing　units　(GPUs)　share　off-chip　main　memory,　CPU　and　GPU　applications　compete　for　the　critical　memory　resource.　This　causes　serious　resource　competition　and　has　a　negative　impact　on　the　overall　performance　of　the　system.　We　describe　the　competition　for　shared-memory　resources　in　a　CPU-GPU　heterogeneous　multi-core　architecture,　and　a　shared-memory　request　scheduling　strategy　based　on　perceptual　and　predictive　batch-processing　is　proposed.　By　sensing　the　CPU　and　GPU　memory　request　conditions　in　the　request　buffer,　the　proposed　scheduling　strategy　estimates　the　GPU　latency　tolerance　and　reduces　mutual　interference　between　CPU　and　GPU　by　processing　CPU　or　GPU　memory　requests　in　batches.　According　to　the　simulation　results,　the　scheduling　strategy　improves　CPU　performance　by　8.53%　and　reduces　mutual　interference　by　10.38%　with　low　hardware　complexity.