Transitioning Scientific Computations to Novel AI Hardware: the Example of Cardiac Electrophysiology Simulations

Recent advances in personalized arrhythmia risk prediction show that computational models can provide not only safer but also more accurate results than invasive procedures. However, biophysically accurate simulations require solving linear systems over fine meshes and time resolutions, which require significant computational resources. However, by leveraging sophisticated parallelization patterns as well as non-traditional hardware architectures, it is possible to meet the computational demands of these simulations. A major recent development in computer hardware was the rise of dedicated accelerator hardware for machine learning applications such as the Graphcore IPUs and Cerebras WSE. These processors have evolved from the experimental state into market-ready products, and they have the potential to constitute the next major architectural shift after GPUs saw widespread adoption a decade ago. In this talk, we present the LYNX cardiac electrophysiology simulator and show how such existing scientific simulations can be ported to novel hardware. We then discuss the opportunities and costs of these devices.