  In this paper, we propose a design methodology to partition and map the
neurons and synapses of online learning SNN-based applications to neuromorphic
architectures at {run-time}. Our design methodology operates in two steps --
step 1 is a layer-wise greedy approach to partition SNNs into clusters of
neurons and synapses incorporating the constraints of the neuromorphic
architecture, and step 2 is a hill-climbing optimization algorithm that
minimizes the total spikes communicated between clusters, improving energy
consumption on the shared interconnect of the architecture. We conduct
experiments to evaluate the feasibility of our algorithm using synthetic and
realistic SNN-based applications. We demonstrate that our algorithm reduces SNN
mapping time by an average 780x compared to a state-of-the-art design-time
based SNN partitioning approach with only 6.25\% lower solution quality.
