PipeCNN: An OpenCL-based open-source FPGA accelerator for convolution neural networks

Convolutional neural networks (CNNs) have been employed in many applications, such as image classification, video analysis and speech recognition. Being compute-intensive, CNNs are widely accelerated by GPUs with high power dissipations. Recently, studies were carried out exploiting FPGA as CNN accelerator because of its reconfigurability and advantage on energy efficiency over GPU, especially when OpenCL-based high-level synthesis tools are now available providing fast verification and implementation flows. In this paper, we demonstrate PipeCNN - an efficient FPGA accelerator that can be implemented on a variety of FPGA platforms with reconfigurable performance and cost. The PipeCNN project is openly accessible, and thus can be used either by researchers as a generic framework to explore new hardware architectures or by teachers as a off-the-self design example for any academic courses related to FPGAs.