Sparse Neural Networks (SNNs) have a significant impact on the efficiency of deep learning by reducing computational costs and memory usage while maintaining or enhancing performance. They enable models to utilize only a small number of significant features, which parallels how the human brain processes information, thereby enhancing model generalization in various tasks[2][3].
Pruning, a method used in creating SNNs, allows for the removal of less important parameters without compromising functionality, streamlining networks for specific applications[4]. This has become crucial for deploying AI on resource-limited devices, leading to faster training times and reduced energy expenditure[1][5].
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