## 6 Conclusions  
In this work, we partitioned a Convolutional Neural Network for distributed inference into constrained Internet-of-Things devices using nine different approaches and we propose Deep Neural Networks Partitioning for Constrained IoT Devices (DN\({}^{2}\)PCIoT), an algorithm that partitions graphs representing Deep Neural Network for distributed execution on multiple constrained IoT devices aiming for inference rate maximization or communication reduction. This algorithm adequately treats the memory required by the shared parameters and biases of CNNs so that DN\({}^{2}\)PCIoT can produce valid partitionings for constrained devices. Additionally, DN\({}^{2}\)PCIoT makes it easy to use other objective functions as well.  
We partitioned two versions of the LeNet model with different levels of neuron grouping into five different setups aiming for inference rate maximization. Several approaches were employed for the partitionings, including the per-layer approach, which is the approach offered by popular ML tools such as TensorFlow, DIANNE, and DeepX, and the widely used tool METIS. We compared these approaches to DN\({}^{2}\)PCIoT and showed that either the approaches could not produce valid partitionings for more constrained setups or they yielded suboptimal results, with DN\({}^{2}\)PCIoT achieving up to 38% more inferences per second than METIS. We also calculated the inference rate for a single device of each experiment assuming the memory of this device was sufficient to execute the whole LeNet. We showed that, even if it were possible to execute the inference on a single device, there might be performance advantages of distributing its execution among multiple devices such as gains from 1.7 to 4.69 times in the inference rate provided by DN\({}^{2}\)PCIoT. Finally, the results for the inference rate maximization objective function were plotted along with the respective amount of transferred data so that it was possible to see how optimizing for one objective function affects the other. Our results suggest that our tool can 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. The partitionings for both versions of LeNet achieved comparable results, with the less fine-grained LeNet model leading to the best results in 80% of the experiments. Thus, we showed that a less fine-grained model can be used in the partitionings with limited impact on the results.  
Conceptualization, F.M.C.d.O. and E.B.; Data curation, F.M.C.d.O.; Formal analysis, F.M.C.d.O. and E.B.; Funding acquisition, E.B.; Investigation, F.M.C.d.O.; Methodology, F.M.C.d.O. and E.B.; Project administration, F.M.C.d.O. and E.B.; Resources, E.B.; Software, F.M.C.d.O. and E.B.; Supervision, E.B.; Validation, F.M.C.d.O. and E.B.; Visualization, F.M.C.d.O.; Writing--original draft, F.M.C.d.O.; and Writing--review and editing, F.M.C.d.O. and E.B..  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001 and PROCAD 2966/2014 -, by CNPq (142235/2017-2 and 313012/2017-2), FAPESP (2013/08293-7), Microsoft, and Petrobras.  
The authors would like to thank the Multidisciplinary High Performance Computing Laboratory for its infrastructure and contributions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.