Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and resource-constrained remote sensing edge devices. To tackle this problem, this study proposes a lightweight remote sensing detection network suitable for edge devices and an energy-efficient CNN accelerator based on field-programmable gate arrays (FPGAs). First, a series of network weight reduction and optimization methods are proposed to reduce the size of the network and the difficulty of hardware deployment. Second, a high-energy-efficiency CNN accelerator is developed. The accelerator employs a reconfigurable and efficient convolutional processing engine to perform CNN computations, and hardware optimization was performed for the proposed network structure. The experimental results obtained with the Xilinx ZYNQ Z7020 show that the network achieved higher accuracy with a smaller size, and the CNN accelerator for the proposed network exhibited a throughput of 29.53 GOPS and power consumption of only 2.98 W while consuming only 113 DSPs. In comparison with relevant work, DSP efficiency at an identical level of energy consumption was increased by 1.1-2.5 times, confirming the superiority of the proposed solution and its potential for deployment with remote sensing edge devices.
Keywords: CNN accelerator; field-programmable gate arrays (FPGAs); lightweight neural network; remote sensing.