In　recent　years,　with　the　development　of　artificial　intelligence　chip,　brain-like　computing　chip　has　become　one　of　the　hotspots　in　the　field　of　artificial　intelligence　chip.　Brain-like　computing　chip　is　a　micro　nano　photoelectric　device　that　can　simulate　the　functions　of　neurons　and　synapses　by　physical　and　physiological　deconstruction　of　human　brain　or　animal　brain.　Brain-like　chip　is　the　basis　of　other　high-level　artificial　intelligence　tasks.　Because　of　its　excellent　performance　and　the　ability　to　subvert　the　＇memory　wall＇problem　existing　in　von　Neumann　architecture,　it　has　extremely　important　research　significance　in　the　fields　of　artificial　intelligence,　computer　science　and　so　on.　With　the　increasing　need　of　the　hardware　hashrate　and　the　invention　of　advanced　methods,　all　kinds　of　brain-like　smart　chips　have　developed　rapidly.　Thus,　This　paper　aims　to　evaluate　the　developments　and　applications　of　brain-like　computing　chips　with　a　more　thorough　view.　First,　the　basic　structure　and　development　of　class　chip　is　described;　Second,　the　principles,　methods,　process,　structures　and　overall　frameworks　are　introduced　in　detail　based　on　different　brain-like　computing　chip　models,　such　as　Loihi,　TrueNorth,　and　＇Darwin　NPU　2＇.　Besides,　experiments　are　carried　out　according　to　the　Tianjic　chip　model.　The　accuracy,　memory,　time-consuming　and　other　data　of　Tianjic　chip　(＇Tianjic　â.　＇and　＇Tianjic　â.　＇)　and　original　model　tested　with　full　connected　ANNs　and　small-scale　CNNs　are　analyzed　and　compared,　trying　to　figure　out　Complete　and　accurate　deficiencies　and　advantages　of　the　Tianjic　chip.　Finally,　combined　with　the　current　research　progress　of　brain-like　computing　chip,　the　obstacles　existing　in　the　research　of　it　are　summarized,　and　the　future　research　trend　and　main　goals　of　brain-like　computing　chip　technology　are　prospected.　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.