The　development　of　Neural　Architecture　Search　(NAS)　is　hindered　by　high　costs　associated　with　evaluating　network　architectures.　Recently,　several　zero-cost　proxies　have　been　proposed　as　a　promising　method　to　reduce　the　evaluation　cost　of　network　architectures　in　NAS.　They　can　quickly　estimate　the　final　performance　of　the　network　in　a　few　seconds　during　the　initial　phase.　However,　existing　zero-cost　proxies　either　ignore　the　network　structure＇s　impact　on　performance　or　are　limited　to　specific　tasks.　To　address　these　issues,　we　propose　a　novel　zero-cost　proxy　called　Skeleton　Path　Kernel　Trace　(SPKT)　that　leverages　the　whole　network　architecture＇s　skeleton　path　structure　information.　We　then　integrate　it　into　an　effective　Bayesian　optimization　for　NAS　framework　called　PATNAS,　and　demonstrate　its　efficacy　on　different　datasets.　The　results　show　that　our　proposed　SPKT　zero-cost　proxy　can　achieve　a　high　correlation　with　the　final　performance　of　the　network　across　multiple　tasks.　Furthermore,　it　can　significantly　accelerate　the　search　process　for　finding　the　best-performing　network　architectures.　©　1979-2012　IEEE.