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English(EN) S4oP: Operator-level Pruning of Structured State Space Models for Resource-Constrained Devices

新的S4oP方法剪枝状态空间模型以提高效率

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为S4oP的新方法，用于剪枝结构化状态空间模型（SSM），包括S4和S4D架构，使其在资源受限设备上更高效。这种算子级剪枝技术将结构化掩码与微调相结合，可以在保持预测性能的同时显著降低推理延迟。实验表明，高达70%的模型算子可以在没有明显精度损失的情况下被剪枝，从而便于在实际的低资源场景中部署SSM。 AI

影响 使得先进的序列模型能够在计算资源有限的设备上部署。

排序理由 该集群包含一篇详细介绍模型优化新方法的学术论文。

在 arXiv cs.AI 阅读 →

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新的S4oP方法剪枝状态空间模型以提高效率

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Marco Deano, Filippo Ziche, Nicola Bombieri ·

    S4oP：面向资源受限设备的结构化状态空间模型的算子级剪枝

    arXiv:2606.18096v1 Announce Type: cross Abstract: Structured State Space Models (SSMs), including the S4 and S4D architectures, have recently emerged as powerful alternatives to attention-based models for capturing long-range dependencies in sequential data. Despite their strong …

  2. arXiv cs.AI TIER_1 English(EN) · Nicola Bombieri ·

    S4oP：面向资源受限设备的结构化状态空间模型的算子级剪枝

    Structured State Space Models (SSMs), including the S4 and S4D architectures, have recently emerged as powerful alternatives to attention-based models for capturing long-range dependencies in sequential data. Despite their strong empirical performance, deploying these models in t…

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