In　recent　years,　with　the　development　of　deep　neural　network　(DNN),　more　and　more　applications　(e.g.,　image　classification,　target　recognition　and　audio　processing)　are　supported　by　it.　However,　the　disadvantage　of　its　own　large　model　makes　it　difficult　to　apply　on　resource-constrained　devices　such　as　mobile　devices.　In　order　to　solve　this　problem,　the　existing　research　and　technology　mainly　focus　on　the　DNN　model　compression　and　the　segmentation　migration　of　the　model.　The　former　is　generally　at　the　expense　of　reducing　accuracy,　and　the　segmentation　of　the　model　has　no　unified　migration　tool　for　the　DNN　model　of　different　applications.　In　this　work,　we　propose　a　universal　neural　network　layer　segmentation　tool,　which　enables　the　trained　DNN　model　to　be　migrated,　and　migrates　the　segmentation　layer　to　the　nodes　in　the　current　network　in　accordance　with　the　dynamic　optimal　allocation　algorithm　proposed　in　this　paper.　The　experimental　results　show　that　the　tool　can　adapt　to　various　neural　networks　with　different　structures　and　perform　optimal　allocation　of　layers　through　algorithm.　When　the　number　of　working　nodes　increases　from　1　to　5,　this　method　can　speed　up　DNN　2-2.5　times,　and　shows　a　good　acceleration　effect.