Solving a steady-state PDE using spiking networks and neuromorphic hardware
This work addresses the problem of applying neuromorphic computing to scientific simulations, offering a potential benchmark for scalable neuromorphic systems, though it is incremental in expanding application domains.
The paper tackled solving a steady-state heat equation by using spiking neural networks on neuromorphic hardware like IBM TrueNorth and Intel Loihi, achieving results that demonstrate the feasibility of this approach for PDEs.
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.