## 2 Related Work
### Classifications by CPU Architecture
From the 1980s until 2002, microcontrollers and Tiny unit processing for real-time embedded vision and dynamic control systems within mobile robotics applications were manufactured using ARM architecture [34]. Afterward, the massive use of the ARM architecture for 8-bit microcontrollers and success in deploying processing complex logic-mathematical tasks, such as embedded vision and real-time control implementation carried out by Tiny devices, allowed the development of 32-bit ARM Cortex embedded devices, which led to the implementation of TinyML applications aimed at solving problems in mobile robotics applications [35]. Embedded systems based on Cortex-M microcontrollers are built under the ARM architecture, with Cortex-M microcontrollers. Applications for digital signal filtering, embedded computer vision, proportional integral derivative (PID) controllers, and real-time image processing are implemented in these kinds of systems [36; 37; 38]. However, ten years ago, the research community turned again to a new optimized architecture specially designed to deploy highly complex logic-mathematical tasks, such as real-time image processing algorithms and AIoT implementations. This architecture is called RISC-V.  
One of the first characteristics observed, along with others mentioned, that made the RISC-V architecture more attractive over the ARM architecture is the lower power consumption in applications with high processing demand [39]. On the other hand, numerous research works address a comparative analysis of the performance of embedded devices built under RISC-V and ARM architectures subjected to the same test scenarios. Recent works show the implementation of Crypto Algorithms [40], \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Embedded system Type** & **Applications Area** & **General Features** & **Characteristics** \\ \hline
8-bit Architectures & N/A 1 & N/A 1 & N/A 1 \\ \hline \multirow{4}{*}{32-bit Microcontrollers} & Real-time dynamic control systems, TinyML, signal processing, FFT analysis, real-time embedded vision & 16 KB-1 MB SRAM, up to 512 KB EEPROM, up to 480 MHz clock frequency & Low power consumption, high cost, optimal performance with high computational processing demand \\ \hline \multirow{4}{*}{64-bit processors} & Smart mobile devices, GPUs, FPGAs, microprocessors, real-time embedded vision & 1 MB–up to 64 GB SRAM, up to 16 MB flash, up to 2.5 GHz clock frequency & Very high power consumption, high cost \\ \hline \hline \end{tabular} 1 N/A: Not Available information.  
\end{table} 
Table 2: Characteristics of RISC-V architectures. using RISC-V and ARM architectures, to demonstrate the performance and fast processing of complex mathematical problems. In particular, in [41], it is described as a redundant number system based on a finite field accelerator for the SIKEp434 specification. A similar approach is presented in [42]. Other works have taken up the analysis of the implementation of TinyML algorithms for convolutional neural networks (CNNs) [43] to compare the number of clock cycles that devices built with the RISC-V and ARM need to carry out the computational task related to CNN. However, although ARM architecture is still present and dominant in commercial and technological applications related to computer vision made on Tiny embedded devices, the RISC-V architecture has the advantage over ARM because it uses an open-source philosophy. While the ARM architecture has a proprietary and licensed instruction set architecture (ISA), the RISC-V architecture has an open-source ISA based on a reduced instruction set computing (RISC) that allows the academic experimentation under no restrictive license and very successful implementation of TinyML algorithms for solving several complex problems present in the computer vision, dynamic control systems and robotics applications. Table 3 provides some examples of RISC-V embedded systems, and Table 4 provides some examples of ARM embedded systems.