## 2 Related Work
### Classifications by CPU Architecture  
One of the most outstanding technological advances in the last ten years, among many other fields of research, in the area of materials science and nanotechnology, has been the improvement of semiconductor devices and the discovery of more efficient materials when providing electronic mobility and the miniaturization capacity that obeys the theory established by [PERSON] [23]. In the field of embedded systems dedicated to data processing and the execution of complex mathematical-logical operations on a large scale, the advent of new technologies has triggered the embedded systems industry to focus its interests on the development of new hardware architectures which, due to electronic miniaturization in semiconductors, solve problems such as very intense processing capacity with high performance, low energy consumption when executing different tasks, and the possibility of being programmed and even reconfigured through the reduction to a more straightforward set of instructions [22]. Research such as [22] demonstrated the efficient application in the mobile robots control assisted by computer vision that implements techniques dealing with single-layer and multi-layer artificial neural networks (ANNs) [3], also known as dense layer ANNs, using microcontrollers with hardware architectures of 32-bit, as well as 64-bit microcomputers. It is proven that low-cost embedded systems for mobile robotics applications have several advantages and capabilities over commercially known and powerful ones such as FPGA Development Boards and dedicated graphics environment processors [24; 25; 26]. One of the key points for embedded systems dedicated to the execution of critical tasks such as real-time controllers, fuzzy logic systems, and image processing, among others, is the possibility of executing one or several of these tasks simultaneously and in a very close approximation to real time [9; 27]. For image processing, in the embedded computer vision for robotics applications, it is important to keep the execution of tasks very close to real time without sacrificing the results of robotic system performance when the image processing is running [28; 29]. The differences between the architectures of embedded systems with microprocessors, microcontrollers, and FPGAs can cause an unbalanced appreciation approach when performing analysis, and comparing them among others in real-time applications for large-scale data control and processing each type of architecture, from its particular constitution, allows us to equate performance and processing methods to obtain the same results. While microprocessors and microcontrollers are capable of executing a set of instructions that allows the implementation of mathematical logic operations at high speed (depending on the clock speed), FPGAs, in contrast, are capable of synthesizing a hardware architecture by reconfiguring its internal elements, primarily logic tables or logic blocks, which contain hundreds or thousands of logic gates interconnected in a specific way. According to the CPU architecture, we establish the following classification:  
1. Advanced RISC Machine (ARM) systems. 2. Reduced Instruction Set Computer version five (RISC-V) systems. Innovative architectures such as RISC-V and ARM [30], specially designed for low-cost and low-energy consumption embedded systems, have managed to position themselves as the most widely used architectures in the field of dynamic systems control, embedded vision, and real-time image processing applications carried out with Tiny devices [31]. However, there are some important differences between these two CPU architectural configurations in embedded systems. Table 1 shows important characteristics of embedded systems with 8-bit, 32-bit, and 64-bit configurations designed with an ARM architecture. Similarly, Table 2 presents the same characteristics classification for embedded systems designed with RISC-V architecture.