## 2 Related Works
The simulation results indicated that this method not only allowed for the execution of the complete AI model (rather than a simplified version), but also achieved significantly lower latency and higher caching ratios at the edge. These results imply that establishing an edge computing ecosystem as a component of an intelligent Internet of Things system based on services and interfaces helpful to the developers is viable. In addition, [PERSON] et al. [26] have published on edge intelligence. In more detail, this work is offering the background of edge AI, in other words, the architectures, frameworks, and deep learning technologies that are used at the network edge. This paper introduces the basic concepts of AI focusing on deep learning, which is the most popular sector of AI, but also describes the motivation and gives definitions of edge intelligence. This work is essentially an analytical review of the architectures, systems, and framework for training edge intelligence models that concludes by identifying future directions and challenges of edge intelligence. In 2019, a framework named Edgent, which utilizes device-edge synergy for collaborative inference in deep neural networks (DNN), with the aim of achieving high inference accuracy and low latency was developed by [PERSON] et al. [27]. The authors after developing this framework implemented it on a desktop PC and a Raspberry Pi and evaluated it based on real work data, and pointed out the efficacy of the Edgent framework. This framework has the ability to compress existing models to accelerate the DNN inference. To address the issue of task offloading, [PERSON] et al. offered a thorough examination of how the edge and cloud may join their computational resources [28]. Its discussions revolve around artificial intelligence, mathematical, and control theory optimization approaches. These can be utilized to meet diverse constraints, limitations, and dynamic problems pertaining to end-to-end application execution. Unlike other studies, this work on task offloading contributes in a twin way. Firstly, it presents a survey on task offloading that covers three sub-fields, namely optimization algorithms, artificial intelligence techniques, and control theory. Secondly, it categorizes the discussed techniques based on various factors, such as the objective functions, granularity level, utilization of edge and cloud infrastructures, and incorporation of mobility depending on the type of edge devices. As far as collaborative task execution goes, [PERSON] et al. developed a framework that enables a collaborative task execution on edge devices [29]. This task execution is based on an auction algorithm that aims to optimize the accuracy and the execution delay. The author of this paper implemented and evaluated the system performance in relation to accuracy and execution delay by implementing a facial recognition application on a Raspberry Pi. The proposed algorithm outperforms several benchmark policies. Among many object recognition models, one of the most popular is the YOLO network which can implement a real-time application at the network edge. [PERSON] et al. [30] presented a benchmark evaluation and analysis of the YOLO object detector on edge class devices. In this work the YOLO object detector was implemented with an NVIDIA Jetson Xavier, an NVIDIA Jetson Nano, and a Raspberry Pi 4 in conjunction with a USB neural network compute processor. Four versions of the YOLO object detector were tested on the three edge devices using videos with different input-size windows. The findings indicated that the Jetson Nano achieves optimal performance compared to the other two edge devices in terms of high performance and cost. In particular, the Jetson Nano gets 15 FPS while running YOLOv4 tiny model. A recent study by [PERSON] et al. developed an object detection system at the network edge that identifies the system-level trade-offs between E2E latency and execution accuracy [16].