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Neuromorphes Computing Bild: ARIC via midjourney

Neuromorphic Hardware | Preprint Now Online

This post has been translated by an AI and may contain translational inaccuracies.
In line with technical progress, including in the development of artificial intelligence (AI), the demands on the underlying computing architectures are increasing. Alternative information processing concepts offer new possibilities for the efficient handling of different types of data. Neuromorphic computing enables new ways of energy-optimized processing of time-dependent information and sensor-based, low-latency interaction between the environment, humans and machines.

Part 2 of the four-part series by Dr. Natalie Rotermund and Jakob Mertes is now available as an English-language preprint.

 

 

 

Abstract

 

In step with technological advances in Artificial Intelligence (AI), the demands placed on underlying computing architectures are increasing rapidly. Neuromorphic computing (NMC) offers novel approaches to energy-efficient processing of time-dependent information and enables low-latency, sensor-proximal interaction between the environment, humans, and machines. Yet its relevance to industry and AI development extends beyond dedicated neuromorphic platforms: core neuromorphic principles—including event-driven processing, temporal coding, and co-located memory and computation—are increasingly being incorporated into mainstream AI hardware and software architectures. Rather than emerging as a unified, standalone paradigm in the near term, NMC appears poised to reshape AI systems through gradual, principle-level integration and hybridization with classical approaches. This essay, the first in a four-part series, introduces the technical foundations of NMC and develops the above argument through a structured comparison of biological information processing, classical artificial neural networks (ANNs), and spiking neural networks (SNNs). Using visual information processing as a concrete illustrative example, we highlight the distinct operating principles of each paradigm and begin to contextualize their respective limitations and potentials. We discuss the hypothesis that the broader impact of NMC will unfold through hybridization rather than replacement and consider implications for industry stakeholders and technology transfer. The series aims to make the potential of NMC accessible and tangible for product development and process innovation. It is offered as a contribution to an ongoing cross-disciplinary dialogue, with particular attention to the European research and innovation ecosystem and the strategic opportunity it represents for technological sovereignty and industrial competitiveness.

→ Read the full-length article on Preprints.org ←

 

This format is offered as part of the EDIH Hamburg with the support of the European Union and the Hamburgische Investitions- und Förderbank.

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