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Advanced Computing Architectures (ACA)

Towards multi-scale natural-density neuromorphic computing

In everyday tasks such as object recognition in natural environments, brains outperform traditional (von-Neumann) computers in terms of computational capacity, robustness, processing speed and energy efficiency. Neuromorphic computing refers to advanced computing architectures exploiting brain principles underlying this superior performance. Two decades of research in neuromorphic computing have put Europe at the top of technological development. Current challenges in this field are the realization of the complex high-density connectivity of the brain, the resulting communication between network elements, the plasticity of connections and the problem of fast network instantiation in the computing device.

This project aims at overcoming these challenges by assuming a network-centric view. It does not start with the design of the smallest computational elements, but with the analysis of the requirements imposed by the instantiation and ongoing modification of brain-scale networks and the communication therein. The project is thereby targeting a breakthrough: once neuromorphic systems can cope with natural-density networks, connectivity is no longer a barrier for any brain-like computation. Primary test cases are selected from neuroscience because of the fundamental limitations traditional approaches face in this area. Developing a synthetic neuromorphic system by addressing neuroscience questions ensures that the design remains generic and compatible with the principles of nature. To demonstrate applicability for real-world problems, further test cases come from medicine where brain-inspired algorithms such as deep learning open new avenues for diagnostics and therapy.

The consortium is distinguished by a broad range of competences, experts with a decade of experience in prominent European neuromorphic-computing initiatives, and excellent access to a user community. Research in this project extends from semiconductor physics over circuit design and system integration to model description languages up to applications. The partners with existing neuromorphic systems and the developers of simulation code for traditional computers ensure that these systems and the community rapidly and sustainably profit from the results.


Forschungszentrum Jülich GmbH, Germany

RWTH Aachen University, Germany

University of Heidelberg, Germany

University of Manchester, Great Britain

The project is funded by the Helmholtz Association's Initiative and Networking Fund under Grant Agreement SO-092.

The grant period is November 2018 until October 2021.