Spiking Physics-Informed Neural Networks on Loihi 2

Neuromorphic computing platforms hold the promise to dramatically reduce power requirements for calculations that are computationally intensive. One such application space is scientific machine learning (SciML). Techniques in this space use neural networks to approximate solutions of scientific problems. For instance, the popular physics-informed neural network (PINN) approximates the solution to a partial differential equation by using a trained feed-forward neural network, and injecting the knowledge of the physics through the loss function. Recent efforts have demonstrated how to convert a trained PINN to a spiking network architecture. In this work, we discuss our approach to quantization and implementation required to migrate these spiking PINNs to Intel's Loihi 2 neuromorphic hardware. We explore the effect of quantization on the model accuracy, as well as the energy and throughput characteristics of the implementation. It is our intent that this serve as a starting point for additional SciML implementations on neuromorphic hardware.