## 5 Experimental Results
### Inference Rate versus Communication  
Minimizing communication is important to reduce interference in the wireless medium and to reduce the power consumed by radio operations. Common real-time applications that need to process data streams in a small period of time such as anomaly detection from camera images, for instance, the detection of vehicle crashes and robberies, may require a minimum inference rate so that there is no frame loss while reducing communication or even energy consumption is desirable so that the network is not overloaded and device energy life is augmented. On the other hand, applications that process data at a lower rate such as non-real-time image processing may require a small amount of communication so that device battery life is augmented while desirable characteristics are the network non-overload and inference rate maximization. Thus, in this subsection, we want to show how optimizing for one of the objective functions, for instance, inference rate maximization, affects the other, for instance, communication reduction. For this purpose, Figure 7 presents the results of Section 5.1 for the inference rate maximization along with their respective values for the amount of transferred data per inference for each partitioning. We also plotted in these graphs results for the communication reduction objective function, which allow for a fair comparison in the amount of transferred data. For instance, when the objective function is the inference rate, the amount of transferred data may be larger than when the objective function is communication reduction. The inverse may also occur for the inference rate. These results were obtained by executing all the approaches discussed in Section 4, including DN\({}^{2}\)PCIoT 30R and DN\({}^{2}\)PCIoT after the other approaches with the communication reduction objective function. Each graph in Figure 7 corresponds to one setup. In this figure, \"comm\" in the legend parentheses stands for when the approach used the communication reduction objective function, \"inf\" stands for the inference rate maximization objective function, \"free\" stands for the free-input experiment, and \"locked\" stands for the locked-input experiment. It is worth noting that each approach in the legend corresponds to two points in the graphs of Figure 7, one for the execution of LeNet 2:1 and one for LeNet 1:1. DN\({}^{2}\)PCIoT 30R is an exception because it was executed only for LeNet 2:1, thus each approach with DN\({}^{2}\)PCIoT 30R in the legend corresponds to only one point in the graphs. Another exception is the per-layer partitioning, which yielded the same result for both LeNet models and, thus, its results are represented by only one point. In this subsection, we do not distinguish the two LeNet versions employed in this work because our focus is on the approaches and so that the graphs do not get polluted. As we want to maximize the inference rate and minimize the amount of transferred data, the best trade-offs are the ones on the right and bottom side of the graph, i.e., in the southeast position. We draw the Pareto curve [50] using the results for inference rate maximization and communication reduction achieved by all the approaches listed in Section 4 to show the best trade-offs and we divided the graphs into four quadrants considering the minimum and maximum values for each objective function. These quadrants help the visualization and show within which improvement region each approach fell. In Figure 7a, for the two-device experiments, the Pareto curve contains two points, which correspond to the free-input DN\({}^{2}\)PCIoT after METIS for the inference rate maximization and most of the locked-input DN\({}^{2}\)PCIoT after approaches for communication reduction. The only approach that falls within the southeast quadrant is the free-input DN\({}^{2}\)PCIoT after METIS for the inference rate maximization, which is the best trade-off between the inference rate and the amount of transferred data for this setup.