arXiv:2604.22583v2 Announce Type: replace Abstract: Multi-head attention enables Transformers to capture diverse representations, but all attention heads are typically activated for every input, regardless of task complexity. For coarse-grained tasks such as text classification, where relevant information is often global, this fixed allocation can introduce unnecessary computation. We propose BudgetFormer, a Transformer architecture that dynamically allocates attention heads on a per-input basis. The model learns both a head budget and a relevance distribution to select the most informative he
The increasing computational demands of large AI models are driving research into efficiency, making dynamic resource allocation critical for scalable development.
Efficient multi-head attention mechanisms like BudgetFormer can significantly reduce the computational cost and energy footprint of Transformers, enabling larger and more capable models.
AI models can now adapt their computational resources on a per-input basis, leading to more efficient training and inference, especially for tasks with varying complexity.
- · AI compute infrastructure providers
- · Cloud providers
- · AI developers
- · Energy efficiency advocates
- · Inefficient monolithic AI architectures
- · Hardware providers focused solely on raw FLOPs without efficiency considerations
Reduced operational costs for deploying large Transformer models, making advanced AI more accessible.
Acceleration of AI research and development due to lower compute barriers and faster experimentation cycles.
Further commoditization of certain AI capabilities as efficiency gains reduce the economic moat of large compute budgets.
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Read at arXiv cs.LG