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The joint progress of artificial neural networks (ANNs) and domain specifichardware accelerators such as GPUs and TPUs took over many domains of machinelearning research. This development is accompanied by a rapid growth of therequired computational demands for larger models and more data. Concurrently,emerging properties of foundation models such as in-context learning drive newopportunities for machine learning applications. However, the computationalcost of such applications is a limiting factor of the technology in datacenters, and more importantly in mobile devices and edge systems. To mediatethe energy footprint and non-trivial latency of contemporary systems,neuromorphic computing systems deeply integrate computational principles ofneurobiological systems by leveraging low-power analog and digitaltechnologies. SpiNNaker2 is a digital neuromorphic chip developed for scalablemachine learning. The event-based and asynchronous design of SpiNNaker2 allowsthe composition of large-scale systems involving thousands of chips. This workfeatures the operating principles of SpiNNaker2 systems, outlining theprototype of novel machine learning applications. These applications range fromANNs over bio-inspired spiking neural networks to generalized event-basedneural networks. With the successful development and deployment of SpiNNaker2,we aim to facilitate the advancement of event-based and asynchronous algorithmsfor future generations of machine learning systems.

SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning