# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 5 Discussion  
For the teleccare application, it is desired that the hardware cost or computational complexity of the proposed method is affordable to people in rural areas. We use the number of multiply-accumulate (MAC) operations as the performance metric to evaluate the model complexity. In our proposed model, the computation-intensive operation is mainly on the convolutional layer and the fully-connected layer. For a convolutional layer with the kernel size of \(K\), the number of the filter of \(C_{out}\), input length of \(W_{out}\), and the number of input layer \(C_{in}\), the resulting number of MAC at the encoder and decoder side are listed in Tables 8 and 9, respectively. Moreover, the full connection layer at the decoder side has 2992 inputs and 6000 outputs, i.e., the number of MACs is 17,952,000. In summary, the number of GMAC is around 0.08 and 0.06 at the encoder and decoder sides, respectively. Besides, we use a tool called \"SCALE-Sim\" in order to evaluate the required cycles performed on the \"Eyeriss\" chips [45]. For the encoder, the seven \"Conv 1D\" layers listed in Table 8 require 6016, 22,960, 145,386, 118,491, 279,700, 21,088, and 3906 cycles on the Eyeriss chips, respectively. The total number of cycles is about \(5.98\times 10^{5}\) cycles. The ratio of the required number of cycles at the encoder of our method to the MobileUNet, which runs MobileNet [46,47] on U-Net [48], is about one third (see Table 10). It has been shown that MobileUNet implements real-time deep learning in mobile applications [49]. Thus, the model complexity of our proposed model is affordable for a consumer-grade smartphone.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**BER** & **DCT Method @ CR of 15** & **Proposed Method @ CR of 32** \\ \hline  & **PRD** & **QS** & **PRD** & **QS** \\ \hline \(10^{-2}\) & 43.700\% & 0.343 & 7.435\% & 4.304 \\ \(10^{-3}\) & 14.048\% & 1.068 & 5.094\% & 6.282 \\ \(10^{-4}\) & 6.436\% & 2.331 & 4.815\% & 6.646 \\ \(10^{-5}\) & 5.536\% & 2.710 & 4.793\% & 6.676 \\ \(10^{-6}\) & 5.491\% & 2.732 & 4.793\% & 6.676 \\ \(0\) & 5.480\% & 2.737 & 4.792\% & 6.678 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of the impact of communication link quality on the root-mean-square difference (PRD) and the quality score (QS).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer ID** & \(\mathbf{(W_{out},C_{in},C_{out},K)}\) & **Required Number of MAC** \\ \hline Conv 1D (12, 3) & (6000, 1, 12, 3) & 216,000 \\ Conv 1D (32, 5) & (1500, 12, 32, 5) & 2,880,000 \\ Conv 1D (64, 7) & (1500, 32, 64, 7) & 21,504,000 \\ Conv 1D (64, 11) & (375, 64, 64, 11) & 16,896,000 \\ Conv 1D (128, 13) & (375, 64, 128, 13) & 39,936,000 \\ Conv 1D (32, 3) & (187, 128, 32, 3) & 22,978,576 \\ Conv 1D (1, 7) & (187, 32, 1, 7) & 41,888 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The number of multiply-accumulate (MAC) of the convolutional layers at the encoder side.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer ID** & \(\mathbf{(W_{out},C_{in},C_{out},K)}\) & **Required Number of MAC** \\ \hline Conv 1D (32, 3) & (187, 1, 32, 3) & 17,952 \\ Conv 1D (64, 11) & (187, 32, 64, 11) & 4,212,736 \\ Conv 1D (128, 13) & (187, 64, 128, 13) & 19,914,752 \\ Conv 1D (32, 5) & (374, 128, 32, 5) & 7,659,520 \\ Conv 1D (64, 7) & (374, 32, 64, 7) & 5,361,664 \\ Conv 1D (12, 3) & (748, 64, 12, 3) & 1,723,392 \\ Conv 1D (1, 11) & (2,992, 12, 1, 11) & 394,944 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The number of MAC of the deep convolutional layers at the decoder side.