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+have+recently+empowered+various+novel+computer+vision+(CV)+tasks.+In+GNN-based+CV+tasks%252C+a+combination+of+CNN+layers+and+GNN+layers+or+only+GNN+layers+are+employed.+This+paper+introduces+GCV-Turbo%252C+a+domain-specific+accelerator+on+FPGA+for+end-to-end+acceleration+of+GNN-based+CV+tasks.+GCV-Turbo+consists+of+two+key+components%3a+(1)+a+emph%7bnovel%7d+hardware+architecture+optimized+for+the+computation+kernels+in+both+CNNs+and+GNNs+using+the+same+set+of+computation+resources.+(2)+a+PyTorch-compatible+compiler+that+takes+a+user-defined+model+as+input%252C+performs+end-to-end+optimization+for+the+computation+graph+of+a+given+GNN-based+CV+task%252C+and+produces+optimized+code+for+hardware+execution.+The+hardware+architecture+and+the+compiler+work+synergistically+to+support+a+variety+of+GNN-based+CV+tasks.+We+implement+GCV-Turbo+on+a+state-of-the-art+FPGA+and+evaluate+its+performance+across+six+representative+GNN-based+CV+tasks+with+diverse+input+data+modalities+(e.g.%252C+image%252C+human+skeleton%252C+point+cloud).+Compared+with+state-of-the-art+CPU+(GPU)+implementations%252C+GCV-Turbo+achieves+an+average+latency+reduction+of+%252468.4times%2524+(%25244.1times%2524)+on+these+six+GNN-based+CV+tasks.+Moreover%252C+GCV-Turbo+supports+the+execution+of+the+standalone+CNNs+or+GNNs%252C+achieving+performance+comparable+to+that+of+state-of-the-art+CNN+(GNN)+accelerators+for+widely+used+CNN-only+(GNN-only)+models.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.51773bbff487abf894f1cf26893a396f%3frik%3dPJPp8dsyJx8YHQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=GVP+Gnn&simid=608055825569556404&FORM=IRPRST&ck=DEF84356C17044399B8F4A89605FCBA1&selectedIndex=26&itb=0)
