# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 3 Key Considerations of Object Recognition on Edge Computing
### Edge Performance Metrics  
It is essential to evaluate the development of edge AI solutions on metrics beyond just accuracy, including processing time, hardware utilization model size, and power consumption. There are several categories of metric performance, which are AI model performance (Table 7), hardware performance (Table 8), communication and network performance (Table 9), security and reliability (Table 10). Inference accuracy denotes the percentage of correctly classified or detected objects, which ensures reliable recognition results [94]. Some commonly used accuracy metrics are accuracy, F1-score, precision, and recall [95]. Processing time measures the real-time performance of the proposed solutions. It is critical for real-time applications, such as self-driving vehicles, industrial automation, and precision farming. The processing time can be represented by some metrics, such as inference time or latency [96]. Other than processing time, the compute efficiency can also be evaluated using the ratio of floating-point operations per second (FLOPs) and multiply-accumulate operations (MACs) [23]. Lower FLOPs/MACs indicates better efficiency on edge devices. Finally, the model size metrics confirm if the deep learning model is deployable on devices with limited storage [97]. A summary of AI model performance metrics is shown in Table 7.  
Hardware performance metrics measure the computing speed and hardware utilization efficiency. Compute throughput measures the number of inferences per second that hardware can perform. Hardware utilization (such as GPU, TPU, CPU, and RAM utilization) and power consumption (consumed power frame and battery life) are standard metrics that can measure the resource-efficient implementation of edge AI solutions. Thermal efficiency is also an important metric to ensure that the designed hardware operates without overheating. A summary of the key performance metrics is shown in Table 8. Other than the two categories already discussed above, two other metric categories can also be evaluated depending on the implementation case. The communication and network metrics, as shown in Table 9, can be considered for hybrid edge-cloud implementation, especially for remote IoT applications. Meanwhile, the security and reliability metrics, as shown in Table 10, are significant for applications that contains sensitive data, for example, the digital health applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **Key Features** & **Importance** & **Considerations** & **Common Metrics** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Inference \\ Accuracy [94] \\ \end{tabular} } & The percentage of & \multirow{3}{*}{\begin{tabular}{c} Ensures reliable \\ recognition results. \\ \end{tabular} } & Must be balanced with & Accuracy, F1-score, \\  & & & efficiency goals; lower & precision, recall, \\  & & & accuracy may be acceptable & mean average \\  & & & for lightweight models. & precision (mAP). \\ \hline \multirow{3}{*}{\begin{tabular}{c} Processing \\ time [96] \\ \end{tabular} } & The real-time & Improves computation & Critical for real-time & \multirow{3}{*}{\begin{tabular}{c} Inference time \\ (latency), inference \\ time per image \\ \end{tabular} } \\  & performance. & speed significantly. & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Compute \\ Efficiency [23] \\ \end{tabular} } & FLOPs and MACs & \multirow{3}{*}{\begin{tabular}{c} Measures the \\ efficiency of \\ computing process. \\ \end{tabular} } & Lower FLOPs/MACs & \multirow{3}{*}{\begin{tabular}{c} FLOPs/MACs. \\ edge devices. \\ \end{tabular} } \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Model size \\ [97] \\ \end{tabular} } & The total memory & \multirow{3}{*}{
\begin{tabular}{c} Affects deployability \\ size of the model (in \\ MB or GB). \\ \end{tabular} } & & Smaller models are & Stored model size \\  & & & & \\ \cline{1-1}  & & & & (in MB or GB). \\ \hline \hline \end{tabular}
\end{table} 
Table 7: AI model performance metrics.