Convolutional　neural　network　has　become　one　of　the　mainstream　algorithms　of　deep　learning　with　high　computational　resource　consumption.　The　model　inferencing　delay　and　power　consumption　can　be　effectively　reduced　by　the　sparsity-aware　accelerator　with　low　resource　overhead　and　almost　lossless　accuracy.　However,　the　computation　fragmentation　problem　caused　by　sparse　operations　will　greatly　reduce　convolution　operation　efficiency.　In　order　to　alleviate　the　above　problems,　an　input　channel　expansion　method　is　proposed　to　improve　the　resource　utilization　rate　in　this　paper.　Considering　that　bandwidth　often　becomes　the　bottleneck　of　accelerator　performance,　a　bandwidth-efficient　data　loopback　structure　is　designed　to　reduce　data　transmission　between　accelerator　and　off-chip　memory.　The　proposed　hardware　architecture　is　implemented　on　Xilinx　VC709.　It　contains　up　to　1024　multiplication　and　accumulation　units,　providing　409.6　GOP/s　peak　computing　power.　Its　computation　speed　reaches　315.8　GOP/s　in　VGG-16　model,　which　is　equivalent　to　788　GOP/s　of　accelerator　without　sparse　activation　optimization.　54.2%　of　activation　data　transmission　is　canceled　through　data　loopback　bus,　which　eases　the　dependence　on　off-chip　bandwidth.　This　flexible　sparsity-aware　accelerator　architecture　can　be　widely　applied　to　deep　convolutional　neural　networks　for　large-scale　inferencing.　©　2023　Elsevier　B.V.