# Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning
## 5 Experimental Results and Analysis
### Efficiency of the LFP Method  
To evaluate the efficiency of the proposed LFP method, comparisons are made with BODP [12], FTP [14], CoEdge [15], EdgeFlow [24] and the Loc method in terms of MACs, latency, and energy consumption. Additionally, to ensure fairness, local inference (Loc) is set as the baseline, with its master device (MD) fixed as an edge device of type V1. Figure 7 presents the latency results of different algorithms compared across four DNN models. As shown in Figure 7a,b, compared to local inference, the LFP method achieves 3.17\(\times\) and 3.48\(\times\) inference speedups on VGG-16 and VGG-19, respectively. The BODP, CoEdge, and EdgeFlow methods adopt a layer-wise synchronization strategy, which  
Figure 5: Latency metric. Figure 6: Energy metric. results in longer inference times due to frequent synchronization of intermediate results. The BODP method fails to account for the heterogeneity of different edge devices, leading to imbalanced workload distribution and higher inference latency. Both the LFP and FTP methods utilize the fused block strategy to avoid frequent inter-layer synchronization. The LFP method divides the convolutional layers of the DNN model into two fused blocks with low computational redundancy, making it more efficient than the FTP method. As shown in Figure 7c,d, the LFP method achieved 1.65\(\times\) and 2.38\(\times\) inference speedups on ResNet-18 and ResNet-34, respectively. The BODP and CoEdge methods exhibit higher inference latency because they do not apply special processing to the residual blocks in the ResNet architecture, preventing optimal performance during distributed inference. In contrast, the LFP method converts residual blocks into sub-fused blocks, thereby eliminating the synchronization overhead within residual blocks. EdgeFlow encapsulates the layer dependencies of DAG-structured models into carefully partitioned execution units, which are executed in parallel. As shown in Figure 7a,b, the speed of the LFP method becomes 3.17 times on VGG-16 and 3.48 times on VGG-19 compared with that of local inference. The LFP method divides the DNN model's convolutional layers into two fused blocks with low computational redundancy; thus, it is more efficient than the FTP method. By contrast, the FTP method groups all convolutional layers into a single fused block, which becomes overloaded with too many layers, thereby increasing redundancy and reducing efficiency. Furthermore, the BODP method suffers from high latency due to imbalanced workload partitioning and frequent inter-layer synchronization. As shown in Figure 7c,d, the speed of the LFP method becomes 1.65 times on ResNet-18 and 2.38 times on ResNet-34 compared with that of local inference. The LFP method converts the residual blocks in the ResNet model into sub-fused blocks and splits the convolutional layers into two smaller fused blocks, thereby reducing redundancy and improving efficiency. Figure 8 presents the energy consumption experimental results for the comparative methods. As shown in Figure 8a,b, compared to local inference, the LFP method achieves energy savings of 16% and 20% on the VGG-16 and VGG-19 models, respectively. The FTP, BODP, and EdgeFlow methods exhibit higher energy consumption as they focus solely  
Figure 7: Comparison between the latencies of different methods across four DNN models. on latency without considering energy consumption, leading to higher energy costs than local inference. Both CoEdge and LFP consume less energy than local inference, as they take into account the differences in computational and transmission power among edge devices during inference. As shown in Figure 8c,d, the LFP method is less energy-efficient for ResNet compared to local inference because the model's lower computational demand limits the optimization potential of the fused block strategy. The FTP, BODP, and EdgeFlow methods all consume more energy than local inference.