# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 2 System Overview
Edge AI can analyze information locally without exposing it to humans, potentially improving data security. Some studies have reported efforts to improve the security and privacy of edge AI implementation against several threat models [32; 33]. 4. Improved reliability and availability: Edge computing enables AI systems to operate even with intermittent or no internet connectivity. This decentralized nature enhances the reliability and availability of critical AI applications. A recent study by [PERSON] et al. proposed dependency- and topology-aware failure resilience (DTFR), a two-stage scheduler that minimizes failure probability while maintaining low network delay to achieve superior availability performance [34]. 5. Real-time data analysis: Edge AI systems can analyze data in real time, allowing for faster decision making and responses to changing conditions. This is crucial for applications like autonomous vehicles and smart homes, which require immediate actions based on sensor data. A recent study by [PERSON] et al. reported an inference latency of 15 frames per second (FPS) on the FPGA platform [35]. 6. Energy efficiency: Edge AI can improve efficiency by processing data locally, significantly reducing the amount of data transmitted to a cloud server. It also reduces reliance on energy-intensive data centers. Finally, the deep learning model is executed on resource-constrained edge devices with a lower power consumption. For example,Xiangjie et al. proposed a deep reinforcement learning-based edge computing scheme that can reduce energy costs by more than 15% while ensuring the task can be completed on time [36]. On the other hand, [PERSON] et al. proposed a novel middleware for adaptive and efficient split computing that can preserve up to 30% energy while achieving a 16% higher accuracy rate [37]. However, there are some potential challenges in system development that require us to reconsider every approach or method that will be used in the development process, such as the following. 1. Development cost: There is a need to reduce development costs to produce a product with a competitive price. The cost depends on several factors, such the selection of the device, the development time, and the hiring of engineers with special skills. 2. The demand of the applications: Every application demands different requirements. For example, autonomous driving systems requires the real-time performance [6], while the autonomous drones require an energy-efficient system [38]. 3. Human resources: There are some development approaches that requires special expertise. For example, hardware design expertise is needed for FPGA system development. This expertise comes with a steep learning curve compared to other systems' development, which will impact the development cost. 4. Ever-changing systems requirements: There rapid evolution of industry standards requires the system to be adaptable to requirement changes. Some requirements include accuracy performance, hardware performance, security and privacy, and interoperability [39]. 5. Performance evaluation: There is a need to achieve a good balance some parameters, such as accuracy, latency, energy consumption, security, and reliability, depending on the application. Therefore, there is a need for good metrics that can represent an ideal implementation of edge AI [40]. 6. Thermal driven performance collapse: Mobile GPU on edge devices can be leveraged to improve the processing performance of deep neural networks (DNNs). However, this consumes a large amount of energy. After a short period of time, the mobile device may become overheated and the processors are forced to reduce the clock speed, significantly reducing the inference speed [41]. 7. Accuracy and latency attack on edge AI: Some studies reveal that the implementation of edge AI for objective recognition is also prone to the accuracy and latency attacks. Adversarial attacks introduce carefully crafted changes to the input data that can reduce the accuracy performance of a deep learning model [20].