# Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning
## 5 Experimental Results and Analysis
### Effectiveness of the LFP Method  
The effectiveness of the LFP method was verified on the basis of the three evaluation metrics described in Section 5.1. To achieve a balanced optimization of both latency and energy consumption, equal weights were assigned to both metrics: \(\lambda_{t}=\lambda_{e}=0.5\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **CPU** & **Memory** &
\begin{tabular}{c} **Number of** \\ **Instances** \\ \end{tabular} \\ \hline V1 & 4 Core Intel Xeon Gold 6149 2.5 GHz (Intel, & 4 GB & 2 \\  & Santa Clara, CA, USA) & \\ V2 & 8 Core Intel Xeon (Skylake) Platinum 8163 & 8 GB & 2 \\  & 2.5 GHz & \\ V3 & 8 Core Intel Xeon Platinum 8369 3.3 GHz & 16 GB & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Virtual machine configuration in experiments.  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Parameter** & **Value** & **Definition** \\ \hline \(B\) & 125 & Total channel bandwidth (MB/s) \\ \(N\) & 5 & Number of edge device \\ \(P_{comm,i}\) & [10, 10, 12] & The computing power (W) of different types \(U_{i}\) \\ \(P_{comp,i}\) & [2, 2, 3] & The transmission power (W) of different types \(U_{i}\) \\ \(p\) & [17, 19, 6, 7] & The partition points for VGG16, VGG19, ResNet18, and ResNet34 \\ \(\lambda_{t},\lambda_{e}\) & (0.5, 0.5) & The weights of latency and energy consumption \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings for edge-device simulation experiments.  
Figure 4 illustrates the impact of not using distributed inference or the two-stage fusion block strategy on the performance of the LFP method across four models. Here, the Loc method represents local inference, while the NLF method indicates the absence of the two-stage fusion block strategy. In the figure, V1, V2, and V3 correspond to the edge devices listed in Table 1. For all four models, the proposed LFP method exhibits the lowest MACs, followed by the NLF method. This is because the LFP method partitions the convolutional layers of the DNN model into two segments, creating fusion blocks with lower computational redundancy. In contrast, the Loc method performs inference solely on a single edge device, which does not reduce the MACs.  
The experimental results for the latency metric are shown in Figure 5. The proposed LFP method achieved the lowest latency across the four DNN models. This indicates that, according to the optimization model presented in this paper, the LFP method can derive the optimal workload partitioning strategy. This effectively utilizes other participating edge devices in distributed inference to accelerate the overall inference process.  
The experimental results for the energy consumption metric are shown in Figure 6. The proposed LFP method exhibits the lowest energy consumption across the four DNN models. This is because the LFP method takes into account the computational capabilities and power characteristics of different edge devices. More powerful edge devices are assigned a greater workload, which helps reduce latency and indirectly lowers energy expenditure.  
Figure 4: MAC metric.