## 1 Introduction  
Infrared detection utilizes the difference between target and background radiation for detection. Compared to visible light detection systems, infrared detection has the advantages of a long detection distance, working day and night, and strong anti-interference capabilities [1]. Long-distanced infrared targets, especially under a sky cloud background, usually show weak and small characteristics [2, 3]. The detection of infrared dim and small targets plays an important role in the field of infrared image processing and has high application value in the field of remote sensing. The current infrared dim small target detection methods are mainly divided into the traditional method based on a model-driven approach and the deep learning method based on a data-driven approach. Traditional methods can be divided into three categories: (1) Filter-based methods [4, 5, 6] and background suppression models [7, 8]: These methods have low computational complexity and mainly complete the detection of infrared dim small targets by calculating the differences in grayscale values and suppressing the background; (2) Methods based on the local contrast of the human visual system [9, 10, 11, 12, 13]: These methods are easy to implement and construct the saliency map of the target through the local difference between the target and the background to achieve the detection of dim small infrared targets; (3) Methods based on the low-rank model [14, 15, 16, 17]: These methodstransform the target detection task into a task completed using sparse low-rank tensors. However, these traditional methods mainly rely on the characteristics of handcrafting and need prior knowledge of the background scenes, which usually have a high false-alarm rate in detection tasks with complex backgrounds and extremely dim targets. As for deep learning methods, the field has experienced rapid development and significant advancements. Researchers design specific function modules and customize network architecture to make the network more suitable for infrared dim small target detection. For example, [18; 19; 20; 21; 22] improved the feature expression ability of small targets by combining traditional methods with deep learning and special network design methods; [23; 24; 25] solved the problem that small targets easily disappear in a deep network by adopting the method of dense nesting and encoder-decoder design; [26; 27] used confrontation learning and a multi-scale attention mechanism to achieve the balance between false alarms and missing detections in infrared dim small target detection; and [25; 28] improved the adaptability of the detection network to the complex background through the design of a smoothing operator and attention module. However, the above research has mainly focused on detection performance, which usually leads to complex structures and high computational complexity. Infrared dim small target detection networks usually need to be deployed on edge devices. It is therefore necessary to consider how to reduce the complexity of the network and transplant the network to the selected edge device for hardware acceleration. Graphics processing units (GPUs) exhibit formidable parallel computing capabilities, rendering them applicable to diverse AI algorithms. However, their relatively high power consumption makes them unsuitable for network deployment in mobile devices with low power requirements. Application-specific integrated circuits (ASICs) are custom-designed for particular AI algorithms, delivering exceptional computational performance while maintaining low power consumption. Nevertheless, they suffer from fixed functionality, limited flexibility, and protracted development cycles [29; 30]. In contrast, heterogeneous FPGA platforms demonstrate balanced performance characteristics, featuring high parallelism, reconfigurability, and energy efficiency, making them a good solution for deploying infrared dim small target detection networks.