## 4 Experimental Results and Discussion
### CIFAR-10 Dataset  
CIFAR-10 is a widely used dataset containing 50,000 RGB images of 32 \(\times\) 32 pixels for training and 10,000 for testing, with 10 different classes. We applied data augmentation techniques, including random cropping to 32 \(\times\) 32 with padding of 4 pixels and random horizontal flipping. The images were transformed into tensors and normalized using PyTorch's mean and standard deviation parameters. **Architecture**: For CIFAR-10, we used four different models, namely Network in Network, AlexNet, ResNet-20, and ResNet-32. **Training Process**: The models were trained on one NVIDIA GeForce GTX 1080 Ti GPU, using a batch size of 32 for 320 epochs. The Adam optimizer was used with an initial learning rate of 3 \(\times\) 10\({}^{-4}\) and a weight decay of 1 \(\times\) 10\({}^{-4}\). The ReduceLROnPlateau scheduler was employed to reduce the learning rate by a factor of 0.001 with a patience of 10 epochs. After the initial training, we decomposed the models using the layer sensitivity method and fine-tuned them for 25-50 epochs with AdamW, using the same hyperparameters. The models were then binarized using the XNOR-net method, trained for 500 epochs, and further fine-tuned using the ReduceLROnPlateau scheduler. **Results**: The accuracy and model size after decomposition and binarization are shown in Table 5. \begin{table}
\begin{tabular}{c c c c} \hline
**Network Type** & _Top-1 (\%)_ & _Params Size (MB)_ & _Compression_ \\ \hline \multicolumn{4}{c}{LeNet-5} \\ \hline FP-Model & 99.06\% & 0.244 & - \\ FP-Tensorized & 98.75\% & 0.116 & 2.1\(\times\) \\ BNN-Model & 99.02\% & 0.11 & 10.8\(\times\) \\ Ours & 98.73\% & 0.067 & 17.9\(\times\) \\ \hline \end{tabular}
\end{table} 
Table 4: LeNet-5 Architectures on MNSIT. **Discussion**: Table 5 presents a performance comparison of various neural network architectures applied to the CIFAR-10 dataset, evaluating both full-precision (FP) models and different tensorized and binarized versions. **Network in Network**: the FP Model achieves the highest accuracy of 87.72% with a parameter size of 3.7 MB [83]. The FP-Tensorized version reduces the parameter size to 1.3 MB, achieving a 2.642\(\times\) compression ratio with only a minor drop in accuracy to 86.64%. The BNN model, using 1-bit quantization, further compresses the model to 0.299 MB with a 12.37\(\times\) compression, although the accuracy decreases to 83.35%. Finally, our proposed method (Ours) achieves the highest compression ratio of 32.74\(\times\), reducing the model size to 0.113 MB while maintaining an accuracy of 82.45%, which is competitive, given the significant compression. **AlexNet**: The FP Model achieves 87.24% accuracy with a parameter size of 91.7 MB, which is quite large. The FP-Tensorized version compresses the model to 9.8 MB, achieving a 9.35\(\times\) compression with only a slight drop in accuracy to 86.68%. The BNN model further reduces the model to 2.85 MB with a 32.2\(\times\) compression but at the cost of lower accuracy, at 81.79%. Our proposed method (Ours) outperforms all others in terms of compression, reducing the model to 0.542 MB with a 169.1\(\times\) compression ratio while maintaining 80.91% accuracy, making it highly efficient for edge devices with limited resources. **ResNet-20**: the FP Model provides the best accuracy of 92.60% with a parameter size of 1.1 MB. The FP-Tensorized version compresses the model to 0.62 MB with a 1.7\(\times\) compression ratio while maintaining 90.98% accuracy. The BNN model reduces the size drastically to 0.047 MB, achieving a 23.40\(\times\) compression but lowering the accuracy to 81.87%. Our method (Ours) compresses the model to 0.0342 MB with a 32.16\(\times\) compression ratio and an accuracy of 80.92%, providing a balance between high compression and acceptable accuracy. **ResNet-32**: the FP model achieves the highest accuracy of 93.53% with a model size of 1.9 MB. The FP-Tensorized version compresses the model to 1.1 MB with a 1.72\(\times\) compression ratio and a slight drop in accuracy to 91.56%.