# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 3 Key Considerations of Object Recognition on Edge Computing
### Hardware Optimization
This approach optimizes the AI model and algorithms alongside the hardware design to maximize performance, resource utilization, and power efficiency. Software optimizations involve model compression techniques such as quantization, pruning, and layer fusion, which reduce the computational load and memory usage for efficient inference. On the hardware side, some hardware optimization techniques mentioned in Table 6 can be utilized [91]. Additionally, on the hardware side, some ready-made FPGA-specific accelerators like the Deep Learning Processing Unit (DPU) can also be used to optimize tasks such as convolutions, activations, and data movement, enabling parallelism and pipelining. Frameworks like Vitis AI simplify the co-design process by allowing pre-trained models to be compiled and deployed on FPGA hardware while providing hardware-specific optimizations. Additionally, developers can create custom accelerators using High-Level Synthesis (HLS) to tailor the hardware for specific AI workloads. The integration of both hardware and software improvements ensures low-latency, real-time object recognition, making this co-design approach ideal for applications like autonomous vehicles, surveillance, and industrial automation. By aligning hardware capabilities with model requirements, edge AI systems achieve both high performance and scalability in resource-constrained environments. Another term that is also used is cross-layer optimization [92]. It involves optimizing multiple layers of the system stack, which are the application layer, the model layer, the framework/runtime layer, firmware, and the hardware layer [93]. They are all optimized jointly rather than in isolation. This ensures efficiency, low latency, and reduced power consumption. Cross-layer optimization is essential in edge AI because it aligns AI models, hardware, and software to work together efficiently. Instead of optimizing each layer separately, collaborative optimization across layers leads to better power efficiency, lower latency, and improved overall system performance. The smart surveillance system serves as a use case example for cross-layer optimization. In the application layer, we can simplify the detection operations to detect only suspicious human movements to remove unnecessary computations. In the model layer, we can utilize a lightweight deep learning model (Table 4) and model compression technique (Table 5) to achieve real-time inference. In the framework/runtime layer, the framework, such as VitisAI or TensorRT, can optimize data flow and fuse layers to accelerate processing. Finally, in the hardware layer, we can use hardware optimization techniques (Table 6) on a custom-made hardware accelerator such as DPU or GPU to process the deep neural network operations efficiently. As a result of joint optimization, we obtain the low-power and real-time object recognition with minimal latency. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Key Features** & **Advantages** & **Limitations** & & **Obtained Results** \\ \hline Pipeline design [83] & \begin{tabular}{l} Breaks the computation into stages, with each stage processed in parallel. \\ \end{tabular}  & Maximizes throughput and reduces latency. & Requires careful resource allocation to avoid bottlenecks. & •