## 1 Introduction  
The daily use of smart devices is a growing trend that arises from the population's need to carry out domestic and work activities with greater efficiency, safety, precision, and comfort. These qualities increase individual productivity, improve organizational teamwork, and optimize time spent completing tasks. There is a pressing need to solve open problems in the academic and industrial fields. These include executing repetitive or insecure tasks with greater precision and in less time without putting human and material resources at risk. This need is a key motivator for the research community to develop microelectronic systems that make up an optimized hardware architecture, also known as embedded systems, which support artificial intelligence (AI) algorithms such as machine learning (ML). In this sense, size, data storage capacity, processing speeds, and power consumption are key hardware characteristics for the optimal embedded system selection. Currently, efficient embedded systems have several applications: monitoring and controlling the temperature inside a room [1,2], and regulation of blood pumping in an artificial heart valve in real time [3], among others. However, the mentioned systems have a high economic cost due to the complexity of the hardware and the complexity of the algorithms of these embedded systems. In addition to that, there is high energy consumption. Powerful microprocessors, dedicated graphics processing units, FPGAs, and digital signal processors are used to improve the computational performance of the embedded systems. However, this solution elevates the cost and the power consumption [4]. For robotics systems, in particular, in the case of dynamic control systems, data processing, and embedded systems for computer vision, the implementation of low-cost embedded systems allows for solving problems related to optimal dynamic response, image processing, recognition, and tracking objects and obstacle detection in real time, among others [5; 6; 7; 8]. In terms of power consumption, it has been proven that high-performance embedded systems require more expensive and complex power systems, such as high-capacity batteries. Sometimes, the power systems' weight and volume are unsuitable for small or medium-sized robots. In the last twenty years, there has been a trend in the embedded computer vision for robotics that addresses problems in controlling mobile and industrial robots using FPGAs [9; 10]. FPGAs are embedded systems that allow a robot to solve tasks efficiently and quickly in real time and, above all, autonomously. However, regarding economics, incorporating FPGA and vision sensors with high-performance power systems tends to increase financing costs [11; 12]. High power consumption, high cost, and the need to be an open-source platform in terms of hardware and software development are troubles that face the community dedicated to the embedded vision systems for robotics. Therefore, researchers in this area have focused on low-cost and open-source hardware architectures in the last five years. Advances in this direction have demonstrated that low-cost hardware-constrained embedded systems are superior when dealing with mobile robotics [13; 14]. Hardware-constrained embedded systems are defined as embedded systems whose hardware characteristics are much more limited than other systems, such as computers, FPGAs, and high-end DSPs. However, they can efficiently solve complicated control and monitoring tasks with lower energy consumption, lower costs associated with their manufacturing, and smaller size [15]. One of the main challenges that currently prevails in embedded systems for robotics applications, specifically in those that incorporate elements for artificial vision, such as sensors or cameras, is the optimization of algorithms capable of performing tasks using ML and, in particular, deep learning (DL). These systems are known as embedded vision systems [15; 16; 17; 18].