# Onboard Processing in Satellite Communications Using AI Accelerators
## 4 AI-Capable Commercial Chipsets
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multicolumn{4}{c}{**Computer Capacity in Operations**} & \multirow{2}{*}{**Power Cons. (W)**} \\ \cline{2-2} \cline{4-5}  & **I8 (Ops)** & & & **FP16 (FLOPs)** & **FP32 (FLOPs)** \\ \hline Myriad & \multirow{2}{*}{NR} & \multirow{2}{*}{NR} & \multirow{2}{*}{NR} & \multirow{2}{*}{NR} & \multirow{2}{*}{1–\(\sim\)2} \\ Family & & & & & \\ \hline Jetson Nano & NR & 512 G & NR & 5–10 \\ \hline Jetson TX2 & \multirow{2}{*}{NR} & \multirow{2}{*}{x} & \multirow{2}{*}{NR} & \multirow{2}{*}{7.5-20} \\ Family & & & & \\ \hline Jetson Xavier & \multirow{2}{*}{21T} & \multirow{2}{*}{x} & \multirow{2}{*}{NR} & \multirow{2}{*}{10–20} \\ NX Family & & & & \\ \hline Jetson AGX & \multirow{2}{*}{30–32 T} & \multirow{2}{*}{10 T} & \multirow{2}{*}{NR} & \multirow{2}{*}{10–40} \\ Xavier Family & & & & \\ \hline \multirow{4}{*}{Jetson Orin} & \multirow{2}{*}{35–50 T} & \multirow{2}{*}{x} & \multirow{2}{*}{x} & \multirow{2}{*}{10–25} \\ NX Family & & & & \\ \cline{2-2} \cline{4-5}  & 20 T Sparse & & & \\ \hline \multirow{4}{*}{Jetson GX} & \multirow{2}{*}{108–170 T} & \multirow{2}{*}{54–85 T} & \multirow{2}{*}{3.3–5.3 T} & \multirow{2}{*}{15–60} \\ Orin Family & & & & \\ \cline{2-2} \cline{4-5}  & 92–105 T Sparse & & & \\ \hline Cloud AI & \multirow{2}{*}{70–400 T} & \multirow{2}{*}{35–200 T} & \multirow{2}{*}{x} & \multirow{2}{*}{15–75} \\
100 Family & & & & \\ \hline \multirow{4}{*}{Instinct MI200} & \multirow{2}{*}{181–383 T} & \multirow{2}{*}{181–383 T} & \multirow{2}{*}{181–383 T} & \multirow{2}{*}{45.3–95.7 T} \\ Family & & & & (Matrix) & 300–560 \\ \cline{1-1} \cline{3-5}  & \multirow{2}{*}{22.6–45.9 T} & \multirow{2}{*}{300–560} \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Trade-off of the AI chipsets and boards under consideration: computing capacity per operation. achieve a high operations per second (_OPs_) rate, as good as the Versal AI chip, increasing the power consumption compared to previous NVIDIA families. It is important to analyze the computer capacity per Watt in order to select the proper chipset for the application. Figure 11 presents this information for the most common integer bytes operators and floating point. The Cloud AI 100 family has better performance regarding byte operations (between 2.8 and 5.33 TOPs/W) followed by the Orin NX family (3.5 and 4 TOPs/W); however, Versal's AI Engines on Edge family exhibits a wide range of performance (0.55 and 2.69 TOPs/W) (INT8). For half-precision floating point (FP16), Cloud AI 100 achieves between 1.4 and 2.6 TFLOPs/W, followed by the Orin AGX family (1.35-1.41 TFLOPs/W); unfortunately, Versal's AI data are not reported for this operation. For single-precision floating point (FP32), Versal's AI Engines achieve the best performance (up to 221 GFLOPs/W), followed by the Instinct MI200 family (between 151 and 171 GFLOPs/W for matrix operations). This comparison proves that Versal AI Edge family has a remarkable and wide range of performance per Watt, being a good solution for AI and ML applications for onboard satellite communications. Although none of the analyzed COTS AI-capable devices are considered space-graded, some studies are emerging on the use of the COTS chipset in space AI applications. The current literature in this field includes benchmark studies and comparisons of the performance of embedded GPUs (NVIDIA Jetson TX2, NVIDIA Jetson Xavier NX), embedded processors (ARM Cortex-based), and FPGA-based devices (Versal ACAP) on space workload operations [31; 39; 40; 41; 42; 43; 44; 45]. The principal documented applications are matrix multiplication, useful in CNN implementation [43; 44]; onboard infrared detection [31]; onboard space weather detection, including coronal mass ejection (CME) and particle detection [45]; and massive MIMO beamforming for 5G New Radio [46; 47].