# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
## 4 Results  
Evaluating the benefits of the GPU@SAT DevKit in terms of development time presents a complex challenge. To effectively measure these advantages, we developed a benchmark derived from a subset of the GPU4S benchmark developed by the ESA [55]. This benchmark includes real-world applications commonly used in CV tasks and DL algorithms on resource-constrained systems in orbit, making it an ideal choice for validating the DevKit and collecting performance data for the GPU@SAT IP core. We analyzed both the time required to develop this benchmark and the expertise needed to execute the process. This  
Figure 3: Application development with and without GPU@SAT DevKit. approach allowed us to concretely assess the impact of the DevKit on simplifying and accelerating development workflows. Table 1 provides a summary of the collected results. The results not only confirm the full functionality of the DevKit but also highlight its advantages for application development. The presented benchmark, consisting of six kernels across 18 configurations, required just one man-day of development time, whereas the previous workflow would have taken several man-days to complete. The key takeaway lies in the comparison between the old and new development processes described in Section 3.3. Previously, even simple applications required hours to develop, while more complex ones demanded several man-days of work. With the DevKit, simple applications now take only a few minutes to develop, and complex ones can be completed within a few hours. Moreover, in the new workflow, the user does not need to be proficient in C coding, making application development even more user-friendly. This helps the development process, enabling a broader range of users, including those with less specialized programming skills, to contribute to the innovation and deployment of the applications. In addition to benchmarking individual kernels, we also evaluated the DevKit's performance on a full NN inference task. Specifically, we considered the network included within the GPU4S benchmark repository, which is based on the CIFAR-10 dataset. This evaluation is crucial for assessing how the DevKit handles more complex, real-world scenarios like running entire DL models, typically used in satellite-based image processing or object detection tasks. Notably, these results align closely with those obtained for the inference of the same network on the METASAT platform [62], as detailed in [57]. While the FPGA implementation platform for METASAT differs, this comparison remains highly relevant, as both are soft-core solutions specifically designed for onboard processing in space applications. Table 2 presents the results obtained in terms of Frames Per Second (FPS) and compares them to the results of METASAT. Overall, the execution of entire networks benefits significantly from the DevKit's streamlined workflow. It is important to highlight that GPU@SAT is an IP core that can be implemented on space-grade FPGAs due to its low resource utilization, as shown in Table 3. Notably, end-to-end network inference--previously requiring considerable manual optimization and