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

## TL;DR

This paper explores how brain-inspired principles can lead to more energy-efficient AI technologies to reduce the environmental impact of AI infrastructure.

## Contribution

The paper proposes neurobiological principles as a novel source of inspiration for developing energy-efficient NeuroAI.

## Key findings

- AI infrastructure is consuming vast resources with significant climate impacts.
- Neurobiological principles offer under-exploited inspiration for energy-efficient computing.
- Partnerships between industry and academia are needed to advance this research.

## Abstract

Since the advent of widely accessible AI tools, AI technology has been in high demand by businesses, academic researchers and individuals. Technology companies are building AI infrastructure at a rapid pace, and these facilities consume vast and growing resources, particularly electricity and water, with significant real and projected climate impacts. There is a need for new research initiatives to support long time horizon efforts to develop energy efficient computing capabilities to support the continued growth of AI infrastructure in a sustainable fashion. Such efficiency is required at both the hardware and software levels. Where can industry turn for examples of ultra-low power, energy efficient computing? We argue here that neurobiological principles offer rich and under-exploited sources of inspiration for energy efficient NeuroAI, and that new partnerships between industry and academia should be developed in this direction.