## 5 Discussion
### AI Chipset Selection
#### 5.2.2 Future Proof of Concept  
Currently, as mentioned above, the interest of industry and academia is focused on the implementation of AI for satellite systems, especially for implementation onboard the satellite (see architecture in Figure 3). In that sense, proof-of-concept tests evaluate the performance of AI chipsets in space conditions, where factors such as tolerance to high radiation levels play an essential role [36].  
Several tests are planned to evaluate the feasibility of implementing AI chipsets in space, such as the SPAICE project. The SPAICE project aims to study [35], develop and validate Artificial Intelligence (AI)-based signal processing techniques for satellite communications in scenarios and use cases where specific AI processors can significantly improve performance over the current state-of-the-art. The SPAICE project has the AI-enabled satellite telecommunications testbed as its main deliverable, which will be the platform for testing and demonstrating AI-accelerated scenarios.  
NASA also plans to launch the TechEdSat-13 CubeSat to low Earth orbit in 2022 and deploy from Virgin Orbit's LauncherOne [36]. This three-unit CubeSat, which weighs 2.5 kg, is packed with technologies to test new capabilities in flight. This includes an AI/ML subsystem with the Intel Loihi neuromorphic processor. Loihi is an advanced silicon chip that mimics the function of the human brain. TechEdSat-13 will perform this chip's first orbital flight test and the AI/ML subsystem. This will lay the groundwork for many AI/ML science and engineering applications for space platforms in the future.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**ML Framework/AI Chipset** & \begin{tabular}{l} **Intel Movidius** \\ **Myriad** \\ \end{tabular} & **Nvidia Jetson** & \begin{tabular}{l} **Qualcomm** \\ **Cloud AI** \\ \end{tabular} & \begin{tabular}{l} **AMD** \\ **Instinct** \\ \end{tabular} & \
\begin{tabular}{l} **Lattice** \\ **Sens AI** \\ \end{tabular} &
\begin{tabular}{l} **Xilinx Versal** \\ **AI Core** \\ \end{tabular} \\ \hline TensorFlow & & & & & & \\ \hline Keras & & & & & \\ \hline CNTK & & & & & \\ \hline Caffe/Caffe2 & & & & & \\ \hline Torch/PyTorch & & & & & \\ \hline MXNet & & & & & \\ \hline Kaldi & & & & & \\ \hline ONNX & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: ML framework and AI chipset compatibility matrix.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**AI Chipset/Trade-Off KPIs** & **Computational Capacity** & **Memory** & **Power Consumption** \\ \hline Intel Movidius Myriad 2 & 1 TOPS & 2 MB & \(\sim\)1 W \\  & & & (DRAM8 GB) \\ \hline Intel Movidius Myriad X & 4 TOPS & 2.5 MB & \(\sim\)2 W \\  & & & (DRAM16 GB) \\ \hline Nvidia Jetson TX2 & 1.33 TOPS & 4 GB & 7.5 W \\ \hline Nvidia Jetson TX2i & 1.26 TOPS & 8 GB & 10 W \\ \hline Qualcomm Cloud AI 100 & +70 TOPS & 144 MB & \(\sim\)15 W \\ family & & & (DRAM 32 GB) \\ \hline AMD Instinct MI25 & +12 TOPS & 16 GB & \(\sim\)20 W \\ \hline Lattice sensAI & +1 TOPS & +1 MB & \(\sim\)1 W \\ \hline Xilinx Versal AI Core family & +43 TOPS & +4 GB & \(\sim\)20 W \\ \hline \hline \end{tabular}
\end{table}
Table 7: Trade-off of the AI chipsets under consideration.