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Updated: Feb 25, 2026
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
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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
Summary
Artificial intelligence (AI) infrastructure growth strains resources. Neurobiological principles offer inspiration for energy-efficient computing, promoting sustainable AI development through industry-academia partnerships.
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.
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