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

Hector Andres Gonzalez, Jiaxin Huang, Florian Kelber, Khaleelulla Khan Nazeer, Tim Hauke Langer, Chen Liu, Matthias Aleander Lohrmann, Amirhossein Rostami, Mark Schöne, Bernhard Vogginger, Timo Wunderlich, Yexin Yan, Mahmoud Akl, Christian Mayr

Keywords: Neuromorphic, efficient ML, edge ML, event-based ML, online-learning, spiking neural networks, DNN

TL;DR: We present the unique hybrid microchip powering the world’s largest neuromorphic supercomputer for event-based and asynchronous Machine Learning

Abstract: 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 asynchronous algorithms for future generations of machine learning systems.

Submission Number: 26