To achieve highly parallel data transmission and computation of convolutional neural network acceleration and generate efficient hardware accelerator design, a hardware design and exploration method based on data-alignment and multi-filter parallel computing was proposed. In order to improve the data transmission and computation speed and adapt to various input image sizes, the method first aligned the data according to the input image size to achieve highly parallel transmission and computation at the data level. The method also used the multi-filter parallel computing method so that different filters can simultaneously convolve the input image to achieve parallel computing at the filters level. Based on this method, mathematical models of hardware resources and performance were formulated and numerically solved to obtain the performance and resource co-optimized neural network hardware architecture. The proposed design method was applied to the single shot multibox detector (SSD) network, and results show that the accelerator on Xilinx Zynq XC7Z045 at 175 MHz clock frequency could achieve the throughput of 44.59 FPS, power consumption of 9.72 W, and power efficiency of 31.54 GOP/(s·W). The accelerator consumed 85.1% and 93.9% less power than the central processing unit (CPU) and graphics processing unit (GPU) implementations respectively. Compared with the exiting designs, the power efficiency of the proposed design increased 20%~60%. Therefore, the design method is more suitable for embedded applications with low power requirements.