Editing Machine Learning at the Edge (section)

==='''Optimizing Workload Partitioning'''===
The key idea for much of the optimization done in machine learning on edge systems involves fully utilizing the heterogenous devices that are often contained in these systems. As such, it is important to understand the capabilities of each device so as to fully utilize its advantages. Devices can very greatly from smartphones with more powerful computational abilities to raspberry pis to sensors. More difficult tasks are offloaded to the powerful devices, while simpler tasks, or models that have been somewhat pretrained can be sent to the smaller devices. In some cases, as in [https://ieeexplore.ieee.org/abstract/document/8690980 Mobile-Edge] [2], the task may be dropped altogether if the resources are deemed insufficient. In this way, exceptionally difficult tasks do not block tasks that have the ability to be executed and therefore the system can continue working.