Automated　model　generation　(AMG)　is　an　automated　artificial　neural　network　(ANN)　modeling　algorithm,　which　integrates　all　the　subtasks　(including　adaptive　sampling/data　generation,　model　structure　adaptation,　training,　and　testing)　in　neural　model　development　into　one　unified　framework.　In　existing　AMG,　most　of　the　time　is　spent　on　data　sampling　and　model　structure　adaptation　due　to　the　iterative　neural　network　training　and　the　sequential　computation　mechanism.　In　this　paper,　we　propose　an　advanced　AMG　algorithm　using　parallel　computation　and　interpolation　approaches　to　speed　up　the　neural　modeling　of　microwave　devices.　Efficient　interpolation　approaches　are　incorporated　to　avoid　repetitive　training　of　the　intermediate　neural　networks　during　adaptive　sampling　process　in　AMG.　Parallel　computation　formulation　based　on　a　multi-processor　environment　is　proposed　to　further　save　time　during　interpolation　calculation,　data　generation,　and　model　structure　adaptation　process.　Examples　of　automated　modeling　of　two　microwave　filters　are　presented　to　show　the　advantage　of　this　paper.