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English(EN) Interface-Aware KV Cache Quantization for Dense On-Chip NVM in Long-Context LLM Decoding

新研究通过NVM量化和设备端功耗扩展解决LLM效率问题

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了提高大型语言模型（LLM）效率的方法。第一篇论文《面向长上下文LLM解码的密集片上NVM的接口感知KV缓存量化》提出了一种针对非易失性存储器（NVM）接口优化的量化方案，旨在降低LLM解码的能耗和元数据开销。第二篇论文《PELM：通过推测性解码和动态电压频率调整实现设备端LLM推理的功耗优化》介绍了PELM系统，该系统结合了推测性解码和动态电压频率调整（DVFS）以提高设备端LLM推理的功耗效率，展示了显著的速度提升和能耗降低。 AI

影响 这些研究工作旨在使LLM在部署时更加易于访问和高效，特别是在资源受限的设备上，并降低能耗。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了提高LLM效率的新颖方法。

在 arXiv cs.AI 阅读 →

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新研究通过NVM量化和设备端功耗扩展解决LLM效率问题

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两篇在arXiv上发表的学术论文，详细介绍了提高LLM效率的新颖方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jiahao Zheng, Yifan Qin, Xiaobo Sharon Hu, Yiyu Shi ·

    面向长上下文大模型片上非易失性存储器密集访问的接口感知键值缓存量化

    arXiv:2609.05764v1 Announce Type: cross Abstract: The key-value (KV) cache is the dominant memory bottleneck in long-context large language model (LLM) decoding: every step reads it entirely, so decoding is memory-bandwidth bound. Holding a quantized KV cache in dense on-chip non…

  2. arXiv cs.CL TIER_1 English(EN) · Weisi Yang, Stephen Xia ·

    PELM：利用推测解码和动态电压频率调整实现高能效的设备端 LLM 推理

    arXiv:2609.09662v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased privacy, personalization, and reduced latency. However, LLMs have heavy computation…

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