Binary　Neural　Network　(BNN)　is　a　meaningful　machine　learning　model　on　the　data　plane.　However,　due　to　the　chip　limitations,　the　scalability,　especially　the　number　of　hidden　layers　in　one　pipeline,　is　limited.　For　better　inference　performance,　existing　methods　reuse　the　hidden　layers　through　packet　recirculations.　Recirculations　lead　to　poor　processing　latency.　Additionally,　the　simplified　operations　in　the　in-network　BNN　model　restrict　the　flexibility　of　itself,　which　results　in　the　unarbitrary　input　length　of　neurons　for　more　the　additional　resource　consumption　than　normal　BNN　model.　In　this　paper,　we　present　TBNN,　an　optimized　in-network　BNN　model　that　achieves　both　scalability　and　flexibility.　This　approach　eliminates　deployment　constraints　while　maximizing　hardware　utilization,　advancing　the　feasibility　of　complex　BNN　models　on　resourcelimited　data　planes.　By　replacing　computational　bottleneck　actions　with　Lookup　Tables　(LUTs),　TBNN　enables　at　most　4　×　more　neurons　per　pipeline　and　reduces　per-packet　latency　by　50%　through　minimized　recirculation.　LUT-based　implementation　supports　pruning　operations,　trading　an　accuracy　loss　of　1.69%　for　saving　about　24%　instructions.　©　2025　IEEE.