For　convolution　neural　networks,　increasing　the　performance　of　hardware　computer　systems　is　crucial　in　the　era　of　big　data.　Benefiting　from　the　neuromorphic　devices,　producing　the　convolutional　calculation　at　the　crossbar　array　circuit　has　become　a　promising　approach　for　high-performance　hardware　computer　systems.　However,　as　computation　scales,　this　hardware　system　faces　the　challenge　of　low　resource　utilization　efficiency　and　low　power　efficiency.　Here,　a　novel　pixel-level　strategy,　leveraging　the　dynamic　change　of　electron　concentration　as　the　process　of　convolution　calculation,　is　proposed　for　high-performance　hardware　computer　systems.　Compared　with　the　crossbar　array　circuit-based　strategy,　instead　of　at　least　four　devices,　raised　the　power　efficiency　to　413%　and　decreased　the　training　epochs　to　38%.　This　work　presents　a　novel　physics-based　approach　that　enables　highly　efficient　convolutional　calculation,　improves　power　efficiency,　speeds　up　convergency,　and　paves　the　way　for　building　complex　convolution　neural　networks　with　large-scale　convolutional　computation　capabilities.