# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
## 1 Introduction
#### Edge Computing Onboard Satellites for Space-IoT  
Numerous companies are already leveraging satellite constellations to address the needs of IoT applications. For example, the Iridium NEXT network by Iridium [14] comprises 66 interconnected satellites and is tailored to support IoT devices in remote and maritime environments. Similarly, the constellation of 24 operational satellites by Globalstar [15] provides voice and data services, bridging the gap between satellite and terrestrial networks across various sectors, including maritime, aviation, and emergency services. Orbcomm [16], with a focus on Machine-to-Machine (M2M) and IoT communication, ensures reliable two-way connectivity for asset tracking and remote monitoring in numerous industries. As more companies explore the Space-IoT market [17; 18; 19; 20], there is an increasing drive to reduce satellite size and operational costs while maintaining reliable communication capabilities. This demand has fueled the adoption of pico-satellites, such as _CubeSats_, which typically weigh between 0.1 and 1 kg [21]. These small, lightweight satellites offer significant advantages in terms of cost and energy efficiency. However, their constrained size and power resources necessitate downsizing and optimizing onboard electronics to enable the development of a global Space-IoT infrastructure. A promising solution to these challenges is the deployment of computing power at the _edge_ of the network, i.e., directly on satellites, through the _edge computing_ paradigm. This approach can address many limitations associated with traditional _cloud computing_[22]. By moving computation closer to the data source, edge computing can reduce latency, optimize bandwidth use, enhance reliability and scalability, lower costs, and even improve security [22; 23]. These advantages can be attained through the augmentation of computational capabilities in both _platform computers_--essential for controlling critical functions like power distribution, navigation, and spacecraft guidance--and _payload computers_, enabling them to handle larger volumes of data. Other studies even suggest rethinking the canonical structure of a satellite to make it more suitable as a node in an IoT network [24]. These benefits make edge computing an increasingly attractive strategy for Space-IoT systems. Unsurprisingly, research efforts are more and more focused on advancing edge computing for satellites [25; 26; 27; 28; 29]. Many of these studies underscore the benefits of onboard processing, while also acknowledging the significant challenges posed by the limited computational resources and power constraints inherent to satellite systems [30]. A particularly promising avenue of research is the acceleration of Machine Learning (ML) and Deep Learning (DL) algorithms onboard satellites [31; 32; 33; 34; 35; 36]. This approach opens new possibilities for real-time processing and decision-making in space-based IoT applications. For instance, ref. [36] proposes a novel application scheme for satellite IoT edge computing based on DL. In particular, the authors perform simulations to highlight the significant role played by this approach in image detection. Specifically, they demonstrate how combining edge computing and DL could be beneficial in reducing acquisition time and bandwidth utilization. On the other hand, ref. [34] outlines use cases where onboard Artificial Intelligence (AI) enhances satellite autonomy and control, thereby improving overall Quality of Service (QoS). Nonetheless, the study also highlights the substantial computing power required, which current space-grade processors struggle to meet. Selecting the appropriate hardware for deploying these algorithms is crucial. Commercial Off-The-Shelf (COTS) components are often used to reduce costs and accelerate development timelines. However, these components are frequently unsuitable for the harsh space environment, where resistance to radiation, vibration, and extreme temperatures is critical.