This paper presents the first study of Graphcore's Intelligence Processing Unit (IPU) in the context of particle physics applications. The IPU is a new type of processor optimised for machine learning. Comparisons are made for neural-network-based event simulation, multiple-scattering correction, and flavour tagging, implemented on IPUs, GPUs and CPUs, using a variety of neural network architectures and hyperparameters. Additionally, a K\'{a}lm\'{a}n filter for track reconstruction is implemented on IPUs and GPUs. The results indicate that IPUs hold considerable promise in addressing the rapidly increasing compute needs in particle physics.
@article{arxiv.2008.09210,
title = {Studying the potential of Graphcore IPUs for applications in Particle Physics},
author = {Lakshan Ram Madhan Mohan and Alexander Marshall and Samuel Maddrell-Mander and Daniel O'Hanlon and Konstantinos Petridis and Jonas Rademacker and Victoria Rege and Alexander Titterton},
journal= {arXiv preprint arXiv:2008.09210},
year = {2020}
}