During　image　segmentation　tasks　in　computer　vision,　achieving　high　accuracy　performance　while　requiring　fewer　computations　and　faster　inference　is　a　big　challenge.　This　is　especially　important　in　medical　imaging　tasks　but　one　metric　is　usually　compromised　for　the　other.　To　address　this　problem,　this　paper　presents　an　extremely　fast,　small　and　computationally　effective　deep　neural　network　called　Stripped-Down　UNet　(SD-UNet),　designed　for　the　segmentation　of　biomedical　data　on　devices　with　limited　computational　resources.　By　making　use　of　depthwise　separable　convolutions　in　the　entire　network,　we　design　a　lightweight　deep　convolutional　neural　network　architecture　inspired　by　the　widely　adapted　U-Net　model.　In　order　to　recover　the　expected　performance　degradation　in　the　process,　we　introduce　a　weight　standardization　algorithm　with　the　group　normalization　method.　We　demonstrate　that　SD-UNet　has　three　major　advantages　including:　(i)　smaller　model　size　(23x　smaller　than　U-Net);　(ii)　8x　fewer　parameters;　and　(iii)　faster　inference　time　with　a　computational　complexity　lower　than　8M　floating　point　operations　(FLOPs).　Experiments　on　the　benchmark　dataset　of　the　Internatioanl　Symposium　on　Biomedical　Imaging　(ISBI)　challenge　for　segmentation　of　neuronal　structures　in　electron　microscopic　(EM)　stacks　and　the　Medical　Segmentation　Decathlon　(MSD)　challenge　brain　tumor　segmentation　(BRATs)　dataset　show　that　the　proposed　model　achieves　comparable　and　sometimes　better　results　compared　to　the　current　state-of-the-art.