  Edge inference has become more widespread, as its diverse applications range
from retail to wearable technology. Clusters of networked resource-constrained
edge devices are becoming common, yet no system exists to split a DNN across
these clusters while maximizing the inference throughput of the system. We
present an algorithm which partitions DNNs and distributes them across a set of
edge devices with the goal of minimizing the bottleneck latency and therefore
maximizing inference throughput. The system scales well to systems of different
node memory capacities and numbers of nodes. We find that we can reduce the
bottleneck latency by 10x over a random algorithm and 35% over a greedy joint
partitioning-placement algorithm. Furthermore we find empirically that for the
set of representative models we tested, the algorithm produces results within
9.2% of the optimal bottleneck latency.
