## 5 Experimental Results
### Inference Rate versus Communication
Although several points fell within the southeast quadrant, it is worth noting that the three points that are closest to this best trade-off all correspond to the free-input DN\({}^{2}\)PCIoT for the inference rate maximization, showing the robustness of DN\({}^{2}\)PCIoT. In Figure 7b, for the four-device experiments, the approach that falls both in the Pareto curve and closest to the southeast quadrant is the free-input DN\({}^{2}\)PCIoT after _ikgreedy_ when reducing communication. Therefore, this approach presents the best trade-off for the four-device setup. Six points compose the Pareto curve for the 11-device experiments in Figure 7c. Three of these points falls in the best trade-off quadrant and are the free-input DN\({}^{2}\)PCIoT after _ikgreedy_ for communication reduction and free- and locked-input METIS for inference rate maximization. In this case, the final choice for the best trade-off depends on which condition is more important: if the application requires a larger inference rate, then METIS is the appropriate choice. On the other hand, if the application requires a smaller amount of communication, then DN\({}^{2}\)PCIoT after _ikgreedy_ for communication reduction is a better approach. Figure 7: Inference rate and communication values for: (**a**) 2-device experiments; (**b**) 4-device experiments; (**c**) 11-device experiments; (**d**) 56-device experiments; and (**e**) 63-device experiments; and (**f**) legend for all graphs. Six points also compose the Pareto curve for the 56-device experiments in Figure 7d. In this graph, the approach that falls both in the Pareto curve and closest to the southeast quadrant is the free-input DN\({}^{2}\)PCIoT 30R when maximizing the inference rate. Therefore, this approach presents the best trade-off for the 56-device setup. Finally, in Figure 7e, for the 63-device experiments, the approach that falls both in the Pareto curve and closest to the southeast quadrant is the free-input DN\({}^{2}\)PCIoT 30R when maximizing the inference rate. This approach presents the best trade-off for the 63-device setup. Back to the example of anomaly detection in Section 5.2, in which the application requirements involve a minimum inference rate of around 24 inferences per second while reducing communication is desirable, we can choose the best trade-offs for each setup analyzed in this subsection. In Figure 7a-c, for the setups with 2, 4, and 11 devices, respectively, all the points in the Pareto curve satisfy the application requirement of a minimum inference rate. Thus, we can choose the points that provide the minimum amount of communication. However, in Figure 7d,e, for the setups with 56 and 63 devices, respectively, the points in the Pareto curve with the minimum amount of communication do not satisfy the application requirement of the minimum inference rate. Hence, we have to choose the points with the largest inference rate in the Pareto curve of each setup, which require more communication. These results evidence the lower computational power of the devices used in the 56- and 63-device setup. Our results suggest that our tool also deliver the best trade-offs between the inference rate and communication, with DN\({}^{2}\)PCIoT providing more than 90% of the results that belong to the Pareto curve. DN\({}^{2}\)PCIoT after the approaches or DN\({}^{2}\)PCIoT starting from 30 random partitionings achieved the best trade-offs for the proposed setups, although these approaches only aim at one objective function. Thus, DN\({}^{2}\)PCIoT 30R and DN\({}^{2}\)PCIoT after approaches are adequate strategies when both communication reduction and inference rate maximization are needed, although it is possible to improve DN\({}^{2}\)PCIoT with a multi-objective function containing both objectives.