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].

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

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