To accelerate the forward-propagation process of deep-learning networks, a field-programmable gate array (FPGA) hardware-acceleration system for AlexNet was developed using Vivado High-Level Synthesis (HLS), which can greatly reduce the FPGA development cost. Using Vivado HLS, developers can design hardware architectures on an FPGA platform using C/C++ code instead of a hardware-description language. We implemented AlexNet on an FPGA platform using the HLS tool, and then used the PIPELINE and ARRAY_PARTITION directives to optimize the proposed system. An evaluation of the proposed system shows that its performance is three times better than a traditional computing-platform graphics processing unit (GPU). In the future, owing to the high-level encapsulation, the developed system can be easily transformed into other convolutional neural networks for practical operation, which shows its great portability and practical application value.