## 2 Related Work
### Classifications by CPU Architecture
: _An important issue discussed in the TinyML implementation section is the requirements that the embedded systems selected in this work must meet when deploying TinyML algorithms since this new trend requires that the system have a high capacity for temporary file storage that contains trained and optimized models. The reader will see that the size of the SRAM in each device plays a crucial role in the implementation of TinyML, and it must meet the requirement of being minimal of a specific size._  
**Remark 3**. : _The architecture of the ESP32 boards has allowed experimental applications to be carried out in the last five years for the implementation of classification neural networks, such as real-time image processing, regression, and prediction, likewise, in the case of real-time automatic control systems and estimation of variables that are difficult to measure using sensors and processing complex mathematical models. In [56; 57] are reported results when identifying objects by color and edges using image classifiers through algorithms with neural networks using microcontrollers from the ESP32-S3. Other works, such as [58], show the use of ESP32 microcontrollers to run neural network models for regression and prediction of control signals. Figure 1 shows the ESP32 Wroom 32 block diagram and the ESP32-S3 block diagram._  
Table 3 shows major and typical boards such as Freenove ESP32-S3-WROOM CAM, ESP32 S3-EYE, and Seeed Studio XIAO ESP32S3 Sense that allow implementations of real-time image processing, real-time discrete control systems, TinyML, and TinyDL of very large function in mobile robotics applications. Within the boards recently used in AIoT implementations, boards are based on a Kendryte K210 System on a Chip (SoC) processor, such as the PyAI-K210 board. The Kendryte K210 is an emergent and new type of AIoT SoC processor built under the RISC-V architecture. The SoC incorporates a 64-bit dual-core CPU, which makes it optimal for programming and standalone executed convolutional neural network (CNN) and deep neural network (DNN) algorithms for real-time image classification tasks [49]. Figure 2 shows the K210 SoC block diagram. Figure 1: Block diagram of ESP32-S3 SoC. Another board that gains relevance in AIoT implementations is the Sipeed MAIX-II M2 dock based on a 64-bit single-core Cortex-M7 CPU. This SoC is also built under RISC-V architecture, as is the case of a PyAI-K210 board. The Sipeed MAIX-II M2 dock board is optimal for convolutional neural network (CNN) algorithms implementations and image classifications for mobile robotics applications [49]. In the case of embedded systems with 64-bit architectures, such as Raspberry Pi board, several works addressed the study and implementation of algorithms using the Raspberry Pi that execute real-time image processing and control tasks, as well as detection-tracking object through image classifications with Python and ANN trained using TensorFlow and TensorFlow Lite [54; 59]. Publications such as [60; 61] review the different applications that possibly use the Raspberry Pi platform and program the image processing algorithms in Python by incorporating the OpenCV library. The Raspberry Pi board, designed under an ARM architecture, allows heavy image processing and the possibility of multiprocessing and multithreading applications. These programming techniques are necessary for image processing and other critical tasks running on the same embedded processing environment, such as robotic actuator control. Applications such as the objects detection for their identification and object detection-tracking are used in mobile robotics [62; 63; 64; 65; 11], as well as the patterns detection in human faces [55] using different versions of Raspberry Pi board, such as the Pi 3B+ and Pi 4, mainly. These applications are also implemented in security or warning systems and the Internet of Things, such as those reported in [13].