arXiv:2509.24517v2 Announce Type: replace Abstract: Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics). This sole focus on efficacy has steered development of large-scale models that require massive computational resources, and results in considerable energy consumption and corresponding carbon footprint across the model lifecycle. In this work, we explore how physics inductive biases can offer useful trade-offs between model efficacy and model efficiency (compute, energy, and carbon). We study models with strong, w
The increasing scale and computational demands of large AI models are forcing a re-evaluation of efficiency and sustainability, aligning with broader economic and environmental pressures.
This work directly addresses the unsustainable energy and carbon footprint of current AI development, offering a pathway for more efficient and environmentally responsible AI without sacrificing accuracy.
AI model development will increasingly integrate physics-based inductive biases to achieve better trade-offs between performance and resource consumption.
- · AI compute infrastructure providers
- · Deep learning researchers
- · Industries using spatio-temporal forecasting
- · ESG-focused technology companies
- · Developers solely focused on efficacy gains
- · High-energy-consumption AI architectures
AI models become more computationally efficient and less energy-intensive, reducing operational costs and environmental impact.
This efficiency enables the deployment of complex AI models in resource-constrained environments and broadens AI accessibility.
The reduced energy footprint of AI could mitigate pressure on global energy grids, potentially delaying or lessening the impact of the 'energy bottleneck' on compute scale.
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