# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
## 2 Background and Related Work  
Research has long invested in the use of FPGA-based hardware for accelerating DL algorithms onboard satellites. The work of [44] presents an embedded accelerator for a hybrid Neural Network (NN). This hybrid model integrates a classical Convolutional Neural Network (CNN) for feature extraction with a _spiking fully connected_ stage for classification. The architecture optimizes resource utilization without significantly sacrificing accuracy. Notably, this proposed architecture has been integrated into the European Space Agency (ESA) OPS-SAT experimental satellite, which was launched on 18 December 2019 [45]. In [46], the authors present a novel approach to AI edge processing on Earth Observation (EO) satellites. Unlike the traditional bent pipe model, where images captured by the satellite are transmitted to ground stations for processing when the satellite is in the optimal position, this approach involves storing each image directly in mass memory and processing it on board using an AI engine. The authors also propose potential solutions for deploying Deep Neural Networks (DNNs) on the edge. Specifically, for missions with low to medium dependability requirements, they suggest the use of COTS components like Xilinx Ultrascale+ FPGAs. In [47], the authors propose a hybrid FPGA and System-on-a-Chip (SoC) system for CNN acceleration in the context of spacecraft pose estimation tasks. They use the Xilinx Zynq Ultrascale+ Multi-Processor System-on-a-Chip (MPSoC) as the evaluation platform, exploiting the FPGA's Programmable Logic (PL) to perform inference. The presented implementation runs on two different CNN models and is shown to achieve an accuracy level comparable to a PC-based inference. Research has also been conducted on NanoXplore European radiation-hardened FPGA devices. In [48], the NanoXplore NG-LARGE device is evaluated against Digital Signal Processing (DSP) and Computer Vision (CV) benchmarks. Specifically, the authors present a successful implementation of the full HW/SW processing pipeline for Vision-Based Navigation (VBN), including high-speed data link communication through the SpaceWire protocol. In another paper [49], the researchers propose an FPGA-based accelerator tailored for the inference of the CloudScout CNN [50] and compare its performance to the Movidius Intel Myriad 2 Vision Processing Unit (VPU). The results suggest that while FPGA-based approaches may have higher power consumption and longer time-to-market compared to COTS solutions, they significantly improve inference speed and adaptability. More recently, in [51], the researchers propose CloudSatNet-1, an FPGA-based hardware-accelerated quantized CNN tailored for onboard satellite cloud coverage classification. This model achieves up to 94.4% accuracy and also reduces memory consumption. However, further refinement and testing are needed to improve the classification accuracy on different datasets. Undoubtedly, the growing interest in using FPGAs in space for DL algorithm inference takes advantage of the benefits demonstrated by these and other works [52]. Research indicates that, in certain aspects, FPGAs offer a more cost-effective and versatile solution compared to more powerful alternatives like GPUs [53]. However, the latter typically have higher throughput and peak speed, at the cost of higher power consumption. Despite the significant potential of deploying GPUs in space, particularly for DL algorithms, to our knowledge, there have been no suitable solutions specifically designed for satellites. Previous research [54; 55; 56; 57] has primarily focused on assessing the viability of COTS GPUs, despite their inherent challenges in the space environment. Neuromorphic processors are also emerging as a promising technology for space applications [58; 59]. These processors, designed to mimic the brain's neural architecture, offer ultra-low power consumption and real-time processing capabilities, making them ideal for resource-constrained environments like space.