## 2 Related Work
### Classifications by CPU Architecture
In the Tiny high processing area, specifically in the embedded vision field and real-time control systems implementations, there are embedded systems that in the last three years have obtained high relevance within the research community in the mobile robotic applications, as well TinyML, which are those designed as a hybrid or intermediate step between microcontrollers and digital signal processors, such as the ESP32 devices in their different types and ranges [49]. In particular, the systems using a 32-bit SoC with dual-core architectures allow image processing algorithms to be executed in real time [50; 51], maintaining good performance and low power consumption without compromising other critical tasks that need to be executed on the same system [52]. Works such as [53; 54; 55] show applications in mobile robotics for detecting and monitoring objects in real-time and security systems, as well as the Internet of Things. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Device Name** & \begin{tabular}{c} **Processing** \\ **Hardware System on** \\ **a Chip (SoC)** \\ \end{tabular} & **Flash** & **SRAM** & \begin{tabular}{c} **CPU Clock** \\ **Frequency** \\ \end{tabular} & **CV Applications** \\ \hline \multirow{4}{*}{Raspberry Pi 4} & 64-bit quad-core & \multirow{4}{*}{N/A 1} & \multirow{4}{*}{256 KB} & \multirow{4}{*}{4 \(\times\) 1.5 Ghz} & Robotics, \\  & ARM Cortex A72 & & & & App Development, \\  & processor & & & & AI App, TinyML \\ \hline \multirow{3}{*}{Arduino Portenta} & 32-bit dual core & \multirow{3}{*}{8 MB SDRAM} & \multirow{3}{*}{16 MB NOR} & \multirow{3}{*}{\begin{tabular}{c} M7: 480 MHz/M4: \\ 240 MHz \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Robotics, \\ Embedded Vision, \\ Embedded Vision, \\ Embedded Vision, \\ IA App. \\ \end{tabular} } \\  & STM32H747X1 ARM Cortex (M7 + M4) & & & & \\  & Cortex (M7 + M4) & & & \\ \hline \multirow{3}{*}{Pixy2} & 32-bit dual core NXP & \multirow{3}{*}{2 MB} & \multirow{3}{*}{246 KB} & \multirow{3}{*}{\begin{tabular}{c} M4: 204 MHz/M0: \\ 204 MHz \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Robotics, \\ Embedded Vision, \\ IA App \\ \end{tabular} } \\  & LPC 4330 ARM Cortex (M4 + M0) & & & & \\  & Cortex (M4 + M0) & & & \\ \hline \multirow{3}{*}{Jetson Nano} & 64-bit Quad-core & \multirow{3}{*}{16 GB} & \multirow{3}{*}{4 GB} & \multirow{3}{*}{\begin{tabular}{c} Robotics, \\ Embedded Vision, \\ IA App \\ \end{tabular} } \\  & ARM Cortex-A57 MPCore & & & & \\  & & & & IA App \\ \hline \multirow{3}{*}{OpenMV Cam H7 Plus} & 32-bit single-core & \multirow{3}{*}{32 MB (external), 2 MB (internal)} & \multirow{3}{*}{480 MHz} & \multirow{3}{*}{\begin{tabular}{c} Robotics, \\ Embedded Vision, \\ IA App \\ \end{tabular} } \\  & STM32H7431 ARM Cortex-M7 & & & & \\  & & & & IA App \\ \hline \multirow{3}{*}{Himax WE-I Plus} & 32-bit single-core & \multirow{3}{*}{2 MB} & \multirow{3}{*}{2 MB} & \multirow{3}{*}{400 MHz} & \multirow{3}{*}{\
\begin{tabular}{c} Robotics, \\ Embedded Vision, \\ IA App \\ \end{tabular} } \\  & ARC EM9D DSP with & & & & \\  & FPUCore & & & IA App \\ \hline \multirow{3}{*}{Sony’s Spresense} & 32-bit single-core & \multirow{3}{*}{8 MB} & \multirow{3}{*}{1.5 MB} & \multirow{3}{*}{
\begin{tabular}{c} Robotics, \\ Embedded Vision, \\ IA App \\ \end{tabular} } \\  & CXD5602 ARM Cortex-M4F 6 Core & & & & \\  & & & IA App \\ \hline \hline \end{tabular}  
* N/A: Not Available information.  
\end{table} 
Table 4: Embedded Systems classification for ARM Architecture. **Remark 1**. : _The number and description of the data used for training the DNN can provide information about the system's real behavior, which is relevant when developing ML and TinyML applications. Increasing the amount of data and their type means that the model obtained also has a larger size. This last point must be considered when the DNN is executed on hardware-constrained embedded systems._  
**Remark 2**.