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English(EN) How Much Computation Does Every Forecast Need? Structured Block Skipping in Two Time-Series Foundation Models

时间序列AI模型通过块跳过展示出推理加速潜力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员调查了时间序列基础模型Chronos-Bolt和TimesFM 2.5的计算效率。他们的研究探讨了在推理过程中是否可以移除整个Transformer块而不显著影响预测准确性。虽然在A100 GPU上适度跳过Chronos-Bolt和TimesFM 2.5的块将推理延迟降低了11%以上，但更激进的跳过会导致某些数据集上的性能显著下降。研究结果表明，虽然这些模型中存在结构化冗余，但尚未实现可部署的动态推理方法。 AI

影响 时间序列预测模型在计算成本和推理速度方面具有潜在的降低。

排序理由 该集群包含一篇详细介绍AI模型效率新研究成果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

时间序列AI模型通过块跳过展示出推理加速潜力

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该集群包含一篇详细介绍AI模型效率新研究成果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Max Bourgeat ·

    每次预测需要多少计算量？两种时间序列基础模型中的结构化块跳过

    arXiv:2610.13208v1 Announce Type: new Abstract: Time-series foundation models use the same network depth for every input, although forecasting windows differ in structure and difficulty. We test whether whole Transformer blocks can be removed at inference time in two pretrained m…

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