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。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
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 …
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…