arXiv:2507.01695v3 Announce Type: replace Abstract: Deep neural networks (DNNs) are widely used for their ability to model complex patterns across domains such as computer vision, speech recognition, and robotics. However, larger models, while often more accurate, are computationally expensive and energy-intensive. Since such a cost is typically needed only for challenging inputs, dynamically selecting lighter models for simpler inputs can improve efficiency with minimal impact on accuracy. We introduce PERTINENCE, a runtime method that selects, from a set of pre-trained models, the lightest m
The rapid increase in DNN complexity and associated computational and energy costs is driving innovation in efficiency-enhancing methods.
This development addresses a critical bottleneck in AI deployment by enabling more efficient and less energy-intensive use of large models, particularly at the edge.
AI systems can now dynamically select model complexity based on input, leading to significant improvements in compute efficiency and energy consumption without sacrificing accuracy for simpler tasks.
- · AI hardware manufacturers (for edge/mobile)
- · Cloud providers (reduced compute costs)
- · AI application developers
- · Energy-conscious industries
- · Manufacturers of solely power-hungry AI accelerators
- · Organizations with inefficient AI inference architectures
More widespread and cost-effective deployment of advanced AI models across various devices and environments becomes feasible.
Reduced operational costs for AI services could accelerate adoption in sectors sensitive to energy and compute expenses, leading to new AI-driven product categories.
The freed-up compute capacity or reduced energy demand could ease the 'energy-bottleneck' pressure on AI's growth or allow for the development of even more complex, specialized models.
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Read at arXiv cs.LG