# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 2 System Overview  
In this section, a system overview of edge AI, including the general architecture, benefits, and challenges of the systems, are discussed. Edge computing architectures are designed to bring computation closer to the data sources, reducing latency and improving the performance of AI applications. They involve a hierarchical structure that distributes processing tasks across different layers, including end devices, edge servers, and the cloud. This is shown in Figure 1.  
Edge devices: This layer comprises devices like smartphones, IoT sensors, wearable devices, drones, and surveillance cameras. These devices may have limited computational capabilities but play a crucial role in data collection and potential pre-processing. Edge devices can perform lightweight AI computations locally, reducing reliance on the cloud. Examples include smartphones running facial recognition apps or smart speakers responding to voice commands [25]. Edge AI implementation includes several components, i.e., deep learning models, deep learning frameworks for edge devices, hardware accelerators, task processors, power, and storage. In this paper, we try to emphasize how to optimize edge AI implementation in each of these components. Edge servers: This layer consists of more powerful computing resources located closer to end devices, such as cloudlets, fog nodes, edge servers, routers, base stations,  
Figure 1: General architecture. and IoT gateways. These nodes handle more complex AI tasks that are offloaded from end devices or require the aggregation of data from multiple sources. They can perform tasks like object detection in video streams, data filtering, and local model updates [26]. 3. Cloud: The cloud layer acts as a central hub with substantial computational and storage resources [27]. It is responsible for tasks that require extensive processing power or involve large datasets. These tasks may include training complex deep learning models, performing large-scale data analytics, and managing and orchestrating the overall edge computing infrastructure. The cloud can also provide backup and support for edge nodes, ensuring service continuity. Communication between these layers is crucial for efficient data flow and task distribution. Edge computing frameworks include KubeEdge, OpenEdge, and Azure IoT [28]. Edge computing facilitates this communication and enables the deployment and management of AI services across the edge computing ecosystem. The specific architecture and task distribution depend on the application requirements, device capabilities, and network conditions. It is important to design edge computing architectures that are scalable, flexible, and adaptive to the dynamic nature of edge environments. There are some benefits of using edge computing for object recognition applications [1; 5]. 1. Reduced latency and agile service response: Edge computing reduces latency by processing data closer to the data source, leading to faster response times. This is particularly beneficial for AI applications that require real-time interactions, like autonomous driving, smart cities, and industrial automation. Edge systems serve as nearby computing platforms that process data locally and quickly. Some studies have been developed to optimize latency reduction in edge AI implementation [29; 30]
2. Reduced bandwidth: Edge computing alleviates the burden on the core network by processing data locally, reducing the amount of data transmitted to the cloud. This is especially important with the explosive growth in data generated by IoT devices. It can also reduce reliance on expensive cloud computing resources. A recent study by [PERSON] et al. reported that the implementation of AI in edge computing can reduce bandwidth usage by 51% compared to implementation on the cloud [31]
3. Enhanced privacy and security: Processing sensitive data locally on edge devices reduces the risk of data breaches during transmission to the cloud.