Deployment　of　deep　convolutional　neural　networks　(CNNs)　in　single　image　super-resolution　(SISR)　for　edge　computing　devices　is　mainly　hampered　by　the　huge　computational　cost.　In　this　work,　we　propose　a　lightweight　image　super-resolution　(SR)　network　based　on　a　reparameterizable　multibranch　bottleneck　module　(RMBM).　In　the　training　phase,　RMBM　efficiently　extracts　high-frequency　information　by　utilizing　multibranch　structures,　including　bottleneck　residual　block　(BRB),　inverted　bottleneck　residual　block　(IBRB),　and　expand–squeeze　convolution　block　(ESB).　In　the　inference　phase,　the　multibranch　structures　can　be　combined　into　a　single　3　×　3　convolution　to　reduce　the　number　of　parameters　without　incurring　any　additional　computational　cost.　Furthermore,　a　novel　peak-structure-edge　(PSE)　loss　is　proposed　to　resolve　the　problem　of　oversmoothed　reconstructed　images　while　significantly　improving　image　structure　similarity.　Finally,　we　optimize　and　deploy　the　algorithm　on　the　edge　devices　equipped　with　the　rockchip　neural　processor　unit　(RKNPU)　to　achieve　real-time　SR　reconstruction.　Extensive　experiments　on　natural　image　datasets　and　remote　sensing　image　datasets　show　that　our　network　outperforms　advanced　lightweight　SR　networks　regarding　objective　evaluation　metrics　and　subjective　vision　quality.　The　reconstruction　results　demonstrate　that　the　proposed　network　can　achieve　higher　SR　performance　with　a　98.1　K　model　size,　which　can　be　effectively　deployed　to　edge　computing　devices.　©　2023　by　the　authors.