## 4 New Computing Architectures  
New computing architecture to accelerate computing: Often when processing data, we rely too much on cloud servers, which wastes network bandwidth and consumes time. Thanks to the development of Tensor Processing Unit (TPU), which have become conveniently portable computing devices, we propose novel computing architectures to accelerate emotion recognition and shorten recognition time, in contrast with inputting features directly into the model, we used TPU. The computing framework of this study includes three layers: terminal-side, edge-side, and cloud-side, which effectively integrate the computing resources of the terminal-side \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **CNN** & & **SVC** & & **XGBoost** & **Naive Bays** \\ \hline Modality & Arousal & Valence & Arousal & Valence & Arousal & Valence & Arousal & Valence \\ Accuracy & 81.3\% & 73.2\% & 70.2\% & 78.7\% & 71.0\% & 81.8\% & 54.7\% & 64.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy of each model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research** & **Model** & **Signal Type** & **Emotion Classification** & **Accuracy (AMIGOS)** \\ \hline
[26] & SVM-RBF & GSR & 2 & 65\%(A) 83.9\%(V) \\
[29] & SVM & EEG, ECG & 2 & 68.8\%(A) 67\%(V) \\
[28] & DCNN & ECG, GSR & 2 & 75\%(A), 76\%(V) \\
[52] & XGBoost & EEG, ECG, GSR & 2 & 68\%(A), 80\%(V) \\ Proposed Model & ECG + GSR & 2 & **70.9\%(A), 81.8\%(V)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy comparison with other similar studies. and edge device, to make them work together to complete the computational process of deep learning. Achieve accelerated processing of data, while ensuring data security, user experience, and system availability. Reduce the latency of human-computer interaction, and decentralization. At the same time, effective and reasonable use of terminal-side idle computing resources, edge-side proximity computing resources. Terminal-side: When the raw physiological data are obtained, run the pre-processing decision algorithm, including three values Computing Resource Utilization (CRU) as equation 8 (local-side computing resources, cloud-side current computing resources, and cloud-side predicted resource usage), when the terminal-side (CRU) is more than 0.7 then the raw physiological data will be directly uploaded to the cloud server, the pre-processing and algorithm decision will be run by the cloud, conversely, when the terminal-side computing resource is sufficient, the feature extraction in data pre-processing will be performed by the terminal-side. \[\text{CRU}=\frac{\left(\text{CPU Occupancy rate}+\text{Memory Occupancy} \right)}{2\ \times\ 100\%} \tag{8}\]  
Terminal-side: On the edge-side, we deploy several feature selection algorithms to process features from deep learning or machine learning and pass the streamlined features to the cloud for model decisions  
Cloud side: On the cloud side, we need to collect cloud server computing resources in three seconds, and use machine learning models to predict the resource occupation in the next time period, then calculate the average CRU, in addition to deploying corresponding decision models such as CNN, XGBoost. The data flow is shown in the Figure 4a. When the original data is on the terminal-side, the data pre-processing process will have two cases: when (computing resources) are sufficient, the data pre-processing is performed on the terminal-side, and then passed into the edge-side for feature selection, and finally into the cloud model to produce results; When (capacity value) is insufficient, the data pre-processing is performed directly on the cloud side, after feature selection on the edge-side, and finally the decision is made in the cloud without the participation of the end-side.