FPGA becomes a popular technology for imple-
menting Convolutional Neural Network (CNN) in recent years.
Most CNN applications on FPGA are domain-specific, e.g.,
detecting objects from specific categories, in which commonly-
used 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 ap-
plications on FPGA by transfer learning that adapts pre-trained
models to specific domains, replacing standard convolution layers
with efficient convolution blocks, and applying layer fusion to
enhance hardware design performance. We evaluate TuRF by
deploying a pre-trained VGG-16 model for a domain-specific
image recognition task onto a Stratix V FPGA. Results show
that designs generated by TuRF achieve better performance than
prior methods for the original VGG-16 and ResNet-50 models,
while for the optimised VGG-16 model TuRF designs are more
accurate and easier to process.