PAI-FCNN: FPGA based inference system for complex CNN models

Lansong Diao, Lixue Xia

Abstract: Convolutional Neural Network (CNN) models are becoming complex with advanced OPs and structures, which introduces design challenges for FPGA-based system. In this paper, we present the design of an FPGA-based CNN inference system, PAI-FCNN, to support modern complex CNN models. PAI-FCNN consists of scalable hardware design and a model reconstruction flow in software compiler. In this way, advanced OPs like Deconv, Conv with upsampling, Dilated Conv, Concatenation can be processed by PAI-FCNN with high performance and hardware efficiency. PAI-FCNN also incorporates reduced precision to boost computing capacity, and the emerging CNN-RNN (Recurrent Neural Network) hybrid models are supported. Our experiments on both PC and embedded FPGA platforms show that the system consistently performs in an efficient manner. PAI-FCNN achieves better throughput and power efficiency than GPU solutions.