Editing Machine Learning at the Edge (section)

==='''Dynamic Models'''===
Given the dynamic nature of the environments that edge devices must function, as well as the heterogeneity of the devices themselves, a dynamic model of machine learning is often employed. Such models must keep track of the current available resources including computation usage and power, as well as network traffic. These may change very often depending on the workloads and devices in the system. As such, training models to continuously monitor and dynamically distribute the workloads is a very important part of optimization. Simply offloading larger tasks to more powerful devices may be obsolete if the devices has all of  its computing resources or network capabilities being used up by another workload. 

The way this is commonly done is by using the profiling step described above as a baseline. Then, a machine learning model utilizes the data to estimate the performance of devices and/or layers. During runtime, a similar process is employed which may update the data used and help the model refine its predictions. Network traffic is also taken into account at this stage in order to preserve the edge computing benefit of providing lower latency. Using all of this data and updates at runtime, the partitioning model is able to dynamically distribute workloads at runtime in order to optimize the workflow and ensure each device is utilizing its resources in the most efficient manner. Two very good examples of how such a system is specifically deployed are the Neurosurgeon and EdgeShard systems, shown above.