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FPGA becomes a popular technology for implementing Convolutional Neural Network (CNN) in recent years. Most CNN applications on FPGA are domain-specific, e.g., detecting objects from specific categories, in which commonlyused CNN models pre-trained on general datasets may not be efficient enough. This paper presents TuRF, an end-to-end CNN acceleration framework to efficiently deploy domain-specific applications on FPGA by transfer learning that adapts pre-trained models to specific domains,doi:10.1109/fpl.2018.00033 dblp:conf/fpl/ZhaoNLN18 fatcat:juidwpy2jrgfldzkn3j4pc4mpi