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Advanced Computing Architectures (ACA)
towards multi-scale natural-density Neuromorphic Computing
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towards multi-scale natural-density Neuromorphic Computing

Neuromorphic Computing

In tasks such as object recognition in natural environments, unsupervised learning from few examples and with little reward, or prediction of future actions of other individuals, the brains of humans and other animals outperform traditional computers with respect to computational capacity, robustness against noise and malfunction, processing speed, as well as material cost, size, and energy efficiency. Neuromorphic Computing aims at developing advanced computing architectures with brain-like performance characteristics by exploiting the principles employed by nature. It refers to a new form of information processing based on technologies and algorithms mimicking the structure and dynamics of biological neuronal systems.


Neuromorphic Computing addresses two different application areas: Cognitive computing and Neuroscience simulation. Cognitive computing refers to the field of machine learning, deep learning, and artificial neural networks, i.e,. algorithms and technologies for general purpose applications such as early disease diagnostics or robotics. Neuroscience simulation is used as a tool to study the structure, dynamics and function of biological neuronal systems, and thereby to uncover the principles underlying their superior performance.

Aim of this project

This project is targeting the Neuroscience simulation application area. It is a pilot project preparing a long-term Neuromorphic Computing research initiative. Its main goal is the specification of a future Neuromorphic Computing architecture, including the definition of requirements and target performances, the development of workflows for a systematic validation and benchmarking of neuromorphic architectures, and the development of efficient Neuromorphic Computing concepts, e.g, for the instantiation of and communication within complex neuronal networks or for implementations of single-neuron and synapse dynamics.

Project overview

Network connectivity and communication


Accelerated numerics


System definition, integration and operation


Requirements, validation and benchmarking



Opens new window

This pilot project is funded for three years (Nov 2018 - Oct 2021) by the Helmholtz Initiative and Networking Fund (project no. SO-092), and the Jülich Research Centre.