arXiv:2508.21495v3 Announce Type: replace Abstract: Early-exit neural networks (EENNs) accelerate inference by allowing intermediate classifiers to stop computation once predictions are confident enough. Most methods rely on confidence thresholds for exiting, and consequently, improving classifier calibration is widely assumed to improve performance. In this work, we challenge this assumption and show that calibration alone is not sufficient for EENNs to exploit adaptive computation. To address this insufficiency, we introduce Early-Exit Failure Prediction (EEFP), which accounts for both predi
The continuous drive for more efficient AI inference, especially as models grow larger and deployment cost becomes a bottleneck, fuels research into methods like early-exit neural networks.
Improving the efficiency and reliability of early-exit neural networks can significantly reduce computational costs and latency for AI applications, making advanced AI more accessible and scalable.
The understanding that calibration alone is insufficient for early-exit neural network performance leads to new methods, optimizing for practical adaptive computation rather than just predictive confidence.
- · AI hardware manufacturers (for efficiency gains)
- · Cloud AI service providers
- · Developers deploying large AI models
- · Edge AI computing
- · Inefficient AI inference architectures
Reduced computational demand and faster inference times for certain AI tasks.
Lower operational costs for AI deployment, potentially accelerating broader AI adoption in cost-sensitive applications.
More sophisticated, real-time AI systems capable of operating under strict latency or power constraints, expanding the scope of AI applications.
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Read at arXiv cs.LG