## 1 Introduction  
The integration of edge computing and Artificial Intelligence (AI) has given rise to the Edge AI computing paradigm. Recent breakthroughs in AI model optimizations, the proliferation of Internet of Things (IoT) devices, and the rise of edge computing have established the way for the vast potential of Edge AI. Edge AI is transforming the way IoT devices function by enabling real-time data processing directly on devices rather than relying on distant cloud servers. This shift enables devices to make quicker, more efficient decisions by processing data locally at the \"edge\", on devices like wearables, smart cameras, and autonomous vehicles. As IoT devices become smarter, Edge AI helps to reduce latency, enhance security, and optimize bandwidth usage, while making intelligent, autonomous decisions without need for cloud processing [1]. Edge AI improves numerous applications, such as autonomous vehicles [2; 3; 4], healthcare [2], and smart homes [2; 5] by improving responsiveness, preserving privacy, reducing dependency on centralized infrastructure, and enabling real-time data processing [2]. According to Fortune Business Insights report, the global Edge AI market was valued at USD 20.45 billion in 2023; it is expected to grow to USD 269.82 billion by 2032, reflecting the increasing adoption of Edge AI across industries such as automotive, healthcare, manufacturing, and cybersecurity. As Edge AI proliferates across various domains, effective and high-quality Edge AI solutions become essential as in other computing domains. A critical challenge for Edge AI systems is related to the selection of suitable edge hardware and AI models that meet the specific needs of edge environments. The choice of hardware plays a critical role in ensuring that the edge device can handle the computational demands of AI models while adhering to strict resource constraints, such as limited processing power, memory, and energy [6; 7]. Similarly, selection of AI models is equally important for the overall performance of the system [7]. AI models used in edge environments must be designed to be lightweight, fast, and resource-conscious, which often requires techniques like weight pruning, quantization to reduce model size and computational complexity while maintaining accuracy [8]. In-efficient AI models can degrade the performance of the device, causing delays, excessive power consumption, or a reduction in the overall quality of AI-driven tasks. Thus, the AI models must be tailored to the specific edge device's capabilities. Together, hardware and AI model work in tandem to create a system that is not only capable of performing real-time, intelligent tasks but also operates reliably and efficiently within the constraints of edge environments [8]. However, selecting appropriate edge hardware among a set of candidates that vary from low-power microcontrollers to single-board computer systems; similarly, an appropriate AI model among a set of candidates varying in computational and memory requirements is not a straightforward task [9; 10]. Determining the optimal solutions requires considering several factors regarding the hardware aspects, such as the device's processing power, memory capacity, energy consumption, and physical size, and AI model aspects such as model size, inference speed, resource consumption [6]. Most of the time, decisions on the selection of hardware and the AI models for deployment on the selected edge devices are made by senior practitioners using ad hoc approaches, which can cause inconsistency and inefficiency problems, and make the decision process unjustifiable [9]. Moreover, these approaches may also overlook critical trade-offs (e.g., performance vs. power consumption), and can lead to sub-optimal choices and issues with resource allocation (e.g., more powerful hardware selection for tasks that do not require it, wasting energy and increasing costs; or less capable hardware chosen for tasks that require more computational power, leading to performance bottlenecks).