## 4 Proposed Lightweight AI Model Development Procedures
### Post-Training Weight Quantization on the Selected AI Models  
The state-of-the-art AI models, such as ResNet, DenseNet, EfficientNet, MobileNet, and ShuffleNet, are typically trained using 32-bit floating-point numbers to represent their  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MobileNet Model** & **The Number of Parameters** & **Memory Size** \\ \hline alpha = 0.5 & 833,640 & 10.4 MB \\ \hline alpha = 0.53 & 930,435 & 11.3 MB \\ \hline alpha = 0.75 & 1,839,128 & 21.9 MB \\ \hline alpha = 1 & 3,237,064 & 38.2 MB \\ \hline \hline \end{tabular}
\end{table}
Table 6: The number of parameters and memory size of MobileNet models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ShuffleNet Model** & **The Number of Parameters** & **Memory Size** \\ \hline ShuffleNet & 755,004 & 9.0 MB \\ \hline ShuffleNet & 1,482,368 & 17.5 MB \\ \hline ShuffleNetV3 & 1,991,372 & 23.5 MB \\ \hline \hline \end{tabular}
\end{table}
Table 5: The number of parameters and memory size of ShuffleNet models.  
weights. These floating-point numbers offer high precision and allow for accurate computations during training. However, they can be memory-intensive and computationally expensive when deployed on devices with limited resources. Post-training weight quantization is a technique used to reduce the memory footprint and computational requirements of deep learning models after they have been trained. The process involves representing the trained weight parameters with a lower precision format than the original floating-point representation [27; 28]. By doing so, the model can be more efficiently deployed on resource-constrained devices, such as edge devices, and micro-controller systems, without sacrificing much in terms of performance. In this work, I used 8-bit integer weights to replace 32-bit floating-point weights. Specifically, I used the module of tf.lite.conversion [29] to convert trained TensorFlow models into TensorFlow Lite models. TensorFlow Lite is a framework specifically designed for deploying machine learning models on resource-constrained devices. One of the key features of TensorFlow Lite is support for quantization, which allows models to be converted into a lower precision format to reduce the memory size and improve the inference speed on these devices. It is worth noting that weight quantization may lead to a slight drop in the classification accuracy due to the loss of information caused by the reduced weight precision. The time required for post-training weight quantization is mainly based on two factors: model complexity and the quantization method. More complex models with a higher number of parameters usually take longer to quantize due to the increased computational demands. Different quantization techniques (e.g., uniform quantization, dynamic quantization) have varying computational requirements. Some quantization methods may involve more complex calculations, leading to longer quantization times. In this paper, since I performed 8-bit uniform quantization and the selected AI models do not possess a huge number of parameters, the time required for post-training weight quantization was very short, typically lasting no more than 1 min.