  The widely parallel, spiking neural networks of neuromorphic processors can
enable computationally powerful formulations. While recent interest has focused
on primarily machine learning tasks, the space of appropriate applications is
wide and continually expanding. Here, we leverage the parallel and event-driven
structure to solve a steady state heat equation using a random walk method. The
random walk can be executed fully within a spiking neural network using
stochastic neuron behavior, and we provide results from both IBM TrueNorth and
Intel Loihi implementations. Additionally, we position this algorithm as a
potential scalable benchmark for neuromorphic systems.
