This　paper　forwards　a　neural　network　based　method　on　VLSI　power　estimation.　Power　estimation　technique　was　a　tradeoff　between　precision　and　time.　Simulation　based　power　estimation　gave　the　most　accurate　result　but　time　consuming.　Monte-Carlo　[1][2]　and　other　statistical　approaches　[3][4][5]　estimated　VLSI　power　in　a　less　simulation　dependent　way　and　got　accurate　result　using　less　time.　This　paper　used　neural　network　to　perform　VLSI　power　estimation.　Experiments　were　made　on　ISCAS89　benchmark.　Power　estimation　results　from　[2][3]　were　used　as　training　or　target　vector.　Different　net　structure,　training　plans　and　vector　organizations　were　applied.　For　limited　number　of　test　vector　(number　of　benchmark　circuits),　limited　experimental　results　showed　the　neural　network　based　power　estimation　method　could　give　acceptable　results　with　specific　net　structure.　Power　estimation　runs　faster.　Linear　regression　is　used　to　evaluate　neural　net.　Probabilistic　results　of　regression　R-value　are　observed.　Analysis　shows　that　unfolded　regression　R-value　sample　fit　normal　distribution.　This　method　can　achieve　a　much　faster　power　estimation　result　of　VLSI　on　I/O　and　gate　information　without　simulation　and　analysis　of　detail　structure　and　interconnections.　©　2006　IEEE.