arXiv:2605.25174v1 Announce Type: cross Abstract: Spatial and temporal resource constraints are critical for both biological and artificial intelligent systems. Here we define differentiable cost terms for breadth, depth, and time within a recurrent convolutional neural network conceived as a finite subset of an infinite lattice. We optimize these costs jointly with task errors via backpropagation. We set different pressures on breadth, depth, and time, which leads to diverse computational graphs emerging organically through training. We find that all three resources can be traded off against
The continuous push for more efficient and adaptable AI systems, coupled with increasing computational constraints, drives research into optimizing neural network growth and resource allocation.
This research could lead to more robust and resource-efficient AI models, enabling novel applications in constrained environments and reducing the overall compute footprint of advanced AI.
The ability to organically grow and optimize neural networks across breadth, depth, and time could significantly alter how AI models are designed, trained, and deployed, moving towards more adaptive and less human-intensive architectural selection.
- · AI hardware manufacturers
- · Edge AI providers
- · AI research institutions
- · Robotics companies
- · Organizations relying on static, hand-tuned neural network architectures
More sophisticated and resource-aware AI models become feasible for deployment in various real-world scenarios.
Reduced compute and energy demands for AI training and inference could alleviate current bottlenecks and accelerate AI development.
The development of truly 'living' AI systems that dynamically adapt their structure and resources based on real-time demands could emerge.
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