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Brain-inspired energy efficient technologies for next-generation artificial intelligence.

Hillel J Chiel1,2,3, Jay S Coggan4, Gourav Datta5

  • 1Department of Biology, Case Western Reserve University, Cleveland, USA.

Biological Cybernetics
|February 23, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) infrastructure growth strains resources. Neurobiological principles offer inspiration for energy-efficient computing, promoting sustainable AI development through industry-academia partnerships.

Keywords:
Artificial intelligenceBioFlopEnergy efficiencyNeuroscience

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Last Updated: Feb 25, 2026

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Area of Science:

  • Computer Science
  • Neuroscience
  • Environmental Science

Background:

  • The rapid expansion of artificial intelligence (AI) infrastructure, driven by accessible AI tools, leads to substantial consumption of electricity and water.
  • This resource consumption poses significant environmental challenges and climate impacts, necessitating sustainable solutions for AI growth.
  • Current AI development lacks sufficient focus on energy-efficient computing at both hardware and software levels.

Purpose of the Study:

  • To explore novel approaches for developing energy-efficient computing capabilities to support sustainable AI infrastructure growth.
  • To identify under-exploited sources of inspiration for ultra-low power, energy-efficient computing.
  • To advocate for new collaborative frameworks between industry and academia to advance energy-efficient AI.

Main Methods:

  • Literature review and conceptual analysis of neurobiological principles.
  • Assessment of potential applications of neurobiological concepts to AI hardware and software design.
  • Identification of opportunities for interdisciplinary research and development.

Main Results:

  • Neurobiological principles present a rich, yet under-utilized, source of inspiration for designing energy-efficient AI systems (NeuroAI).
  • Adopting bio-inspired computing paradigms can lead to significant reductions in energy and resource consumption.
  • The study highlights the potential for breakthroughs in ultra-low power computing by emulating biological systems.

Conclusions:

  • Neurobiological principles offer a promising pathway towards sustainable AI development by enabling ultra-low power, energy-efficient computing.
  • Establishing strong partnerships between industry and academia is crucial for translating neurobiological insights into practical AI solutions.
  • Future research should focus on developing energy-efficient NeuroAI by integrating biological computing paradigms into AI infrastructure.