We often explore ways to make deep learning models more efficient. One fundamental insight is that deep learning models are inherently sparse—many weights can be safely neglected and zeroed out without significant accuracy loss. This idea, known as model pruning, was first introduced by Yann LeCun in the 1980s through the pioneering work Optimal Brain Damage. Since then, both software frameworks and hardware accelerators have evolved to take advantage of this sparsity, enabling more efficient inference and reduced memory consumption.

Most pruning methods produce unstructured sparsity, where any individual weight can be zeroed out. While this maximizes flexibility, it poses challenges for hardware acceleration. A more hardware-friendly alternative is 2:4 semi-structured sparsity, where out of every four consecutive weights, exactly two are zero. This pattern strikes a balance between model flexibility and computational efficiency, making it ideal for modern GPU architectures.

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