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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 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 applications 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. and layer fusion to enhance hardware design performance. We evaluate TuRF by end-to-end deploying a pre-trained VGG-16 model for a domain-specific image recognition task onto Stratix V FPGA. Results show that designs generated by our framework achieve better performance than prior works, regarding the original VGG-16 and ResNet-50, and the optimised VGG-16 model is more accurate and easier to process.

Towards Efficient Convolutional Neural Network for Domain-Specific Applications on FPGA