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English(EN) Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware

新方法在 LLM 中诱导稀疏神经活动以用于神经形态硬件

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的方法，可以在量化线性注意力语言模型中诱导稀疏神经活动，在不显著降低性能的情况下显著降低计算成本。该方法将低于可训练阈值的激活值置零，旨在优化大型语言模型以适应神经形态硬件。与边缘 GPU 推理相比，所提出的技术可实现高达 37 倍的吞吐量和 16 倍的功耗降低，使这些稀疏模型成为事件驱动多核平台的理想选择。 AI

影响 这项研究可以实现 LLM 在专用神经形态硬件上更高效的部署，从而降低功耗并提高推理速度。

排序理由 该集群包含一篇详细介绍语言模型优化新方法的学术论文。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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

新方法在 LLM 中诱导稀疏神经活动以用于神经形态硬件

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Simon Richter, Ruhai Lin, Jason Yik, Taylor Kergan, Rui-Jie Zhu, Farshad Moradi, Jason Eshraghian ·

    面向神经形态硬件的稀疏神经活动事件驱动语言模型

    arXiv:2608.30439v1 Announce Type: cross Abstract: Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache and quadratic attention cost. State-space models (SSMs) mitigate this through linear attention and fixed-size recurrent st…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Jason Eshraghian ·

    用于神经形态硬件的具有稀疏神经活动的事件驱动语言模型

    Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache and quadratic attention cost. State-space models (SSMs) mitigate this through linear attention and fixed-size recurrent states, but their large dense linear projections rem…

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