Toward an Efficient Deep Pipelined Template-Based Architecture for Accelerating the Entire 2-D and 3-D CNNs on FPGA

3-D convolutional neural networks (3-D CNNs) are used efficiently in many computer vision applications. Most previous work in this area has concentrated only on design and optimization of accelerators for 2-D CNNs, with few attempts having been made to accelerate 3-D CNNs on FPGA. We find the acceleration of 3-D CNNs on FPGA to be challenging due to their high computational complexity and storage demands. More importantly, although the computational patterns of 2-D and 3-D CNNs are analogous, the conventional approaches that have been adopted for acceleration of 2-D CNNs may be unfit for 3-D CNN acceleration. In this paper, in order to accelerate 2-D and 3-D CNNs using a uniform framework, we first propose a uniform template-based architecture that uses templates based on the Winograd algorithm to ensure the rapid development of 2-D and 3-D CNN accelerators. Then, with the aim of efficiently mapping all layers of 2-D /3-D CNNs onto a pipelined accelerator, techniques are developed to improve the throughput and computational efficiency of the accelerator, including layer fusion, layer clustering, and workload-balancing scheme. Finally, we demonstrate the effectiveness of the deep pipelined architecture by accelerating real-life 2-D and 3-D CNNs on the state-of-the-art FPGA platform. On VCU118, we achieve 3.7 TOPS for VGG-16, which outperforms state-of-the-art FPGA-based CNN accelerators. Comparisons with CPU and GPU solutions demonstrate that our implementation of 3-D CNN achieves gains of up to <inline-formula> <tex-math notation="LaTeX">$17.8\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$64.2\times $ </tex-math></inline-formula> in performance and energy relative to a CPU solution, and a <inline-formula> <tex-math notation="LaTeX">$5.0\times $ </tex-math></inline-formula> energy efficiency gain over a GPU solution.