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

The joint progress of artificial neural networks 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 from thousands of chips.In this work, we present the design and operating principles of SpiNNaker2 systems.Furthermore, we outline a number of machine learning applications that we developed on either the full chip or earlier prototypes.The already available applications range from accelerating artificial neural networks 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.