## 5 Discussion
### AI Chipset Selection  
One of the essential aspects of AI implementation in SatCom systems is the selection of the AI Chipset. In that sense, we have identified several AI accelerators currently available on the market. However, some initial considerations must be made as to which device is best for the chosen architecture [47]: \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Machine Learning Technique** & **Algorithm** & **Training** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{Supervised Learning} & Deep Neural Network & Off-line & Very low computational cost & When the number of beams or resources increases it can have a bad performance \\ \cline{2-5}  & & & It solves the challenges of using NN. For the DRM system, it works only as an intelligent switch that modifies the resources according to the traffic demand & It will require knowledge of the onboard traffic demand, which will add complexity to the payload design. \\ \cline{2-5}  & & & It solves the challenges of using NN. For the DRM system, it works only as an intelligent switch that modifies the resources according to the traffic demand & It order to train, it is necessary to know previously how the traffic demand changes in the service area \\ \hline \multirow{6}{*}{Reinforcement Learning} & Q-Learning & Online & Adapts to traffic demand. Low computer cost & Requires many episodes to converge and can add a large delay to the system \\ \cline{2-5}  & & & Adapts to traffic demand. Good performance for resource management and requires few episodes to converge & High computational cost to converge \\ \cline{2-5}  & & & Adapts to traffic demand. Very good performance for resource management and requires very few episodes to converge & Very high computational cost \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Main advantages and disadvantages of different machine learning techniques for radio resource management in multibeam SatCom systems. * Scalar processing elements (e.g., CPUs) are very efficient at complex algorithms with diverse decision trees and a broad set of libraries, but they are limited in performance scaling. * Vector processing elements (e.g., DSPs, GPUs and VPUs) are more efficient at a narrower set of parallelizable compute functions. Still, they experience latency and efficiency penalties because of an inflexible memory hierarchy. * Programmable logic (e.g., FPGAs) can be customized to a particular compute function, making them the best at latency-critical real-time applications and irregular data structures. Still, algorithmic changes have traditionally taken hours to compile versus minutes. The following available options containing multiple vector processing units suitable for AI or integrated neural network accelerators can be considered for a first evaluation. Some chipsets may also come coupled with other elements such as CPUs or FPGAs. An extensive list can be found in [48]. * Intel Movidius Myriad X VPU: The Intel(r) Movidius(tm) Myriad(tm) X VPU is Intel's first VPU to feature a Neural Compute Engine--a dedicated hardware accelerator for deep neural network inference. The Neural Compute Engine in conjunction with the 16 powerful SHAVE cores and high throughput intelligent memory fabric makes Intel(r) Movidius(tm) Myriad(tm) X ideal for on-device deep neural networks and computer vision applications [49]. This chipset has been already tested and flown in ESA missions Phi-Sat 1 and Phi-Sat 2 (in preparation) integrated into the UB0100 CubeSat Board [50]. * Nvidia Jetson TX2:The Jetson family of modules all use the same NVIDIA CUDA-X(tm) software. Support for cloud-native technologies such as containerization and orchestration makes it easier to build, deploy and manage AI at the edge. Given the specifications of the TX2 version [51], it promises suitability for in-orbit demonstrations.