  The joint progress of artificial neural networks (ANNs) and domain specific
hardware accelerators such as GPUs and TPUs took over many domains of machine
learning research. This development is accompanied by a rapid growth of the
required computational demands for larger models and more data. Concurrently,
emerging properties of foundation models such as in-context learning drive new
opportunities for machine learning applications. However, the computational
cost of such applications is a limiting factor of the technology in data
centers, and more importantly in mobile devices and edge systems. To mediate
the energy footprint and non-trivial latency of contemporary systems,
neuromorphic computing systems deeply integrate computational principles of
neurobiological systems by leveraging low-power analog and digital
technologies. SpiNNaker2 is a digital neuromorphic chip developed for scalable
machine learning. The event-based and asynchronous design of SpiNNaker2 allows
the composition of large-scale systems involving thousands of chips. This work
features the operating principles of SpiNNaker2 systems, outlining the
prototype of novel machine learning applications. These applications range from
ANNs over bio-inspired spiking neural networks to generalized event-based
neural networks. With the successful development and deployment of SpiNNaker2,
we aim to facilitate the advancement of event-based and asynchronous algorithms
for future generations of machine learning systems.
