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July 9, 20260 citationsOpen Access

Neuromorphic Computing: Brain-Inspired Architectures for Efficient AI

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RSRaj Kiran Sharma

Key Points

  • This research aims to explore neuromorphic computing architectures inspired by biological neural systems, focusing on energy efficiency and computation methods.
  • Survey of neuromorphic architectures, including hardware and software systems.
  • Analysis of spiking neural networks and event-driven computation.
  • Evaluation of energy efficiency advantages over von Neumann architectures.
  • Demonstrated superior energy efficiency in neuromorphic systems compared to traditional architectures.
  • Identified benefits of spiking neural networks in event-driven computation.
  • Highlighted implications for deploying AI systems on edge hardware with limited power budgets.

Abstract

This paper surveys neuromorphic computing architectures — hardware and software systems modelled on biological neural organisation. We examine spiking neural networks, event-driven computation, temporal coding, and energy efficiency advantages over conventional von Neumann architectures. Implications for deploying sovereign AI systems on edge hardware with constrained power budgets are analysed.

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Cite This Study

Raj Kiran Sharma (2026) studied this question.

synapsesocial.com/papers/6a4f3a702b81a944af574cabhttps://doi.org/10.5281/zenodo.21250495
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