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Arxiv
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PERTINENCE: Input-based Opportunistic Neural Network Dynamic Execution
- Deep neural networks (DNNs) are widely used for modeling complex patterns in various domains but can be resource-intensive.
- New method PERTINENCE dynamically selects suitable models from a pre-trained set based on input complexity to improve efficiency without compromising accuracy.
- Its genetic algorithm-based approach balances overall accuracy and computational efficiency by optimizing the selection process.
- The method showcased promising results on CIFAR-10, CIFAR-100, and TinyImageNet datasets, achieving comparable accuracy with up to 36% fewer operations than existing state-of-the-art models.
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