English(EN) SSR: Sparse Segment Reduction for Ternary GEMM Acceleration

新的 SSR 方法加速三元 LLM 推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了稀疏分段归约（SSR）方法，这是一种加速三元大型语言模型（LLM）推理的新方法。该方法优化了三元权重（使用三元值压缩且通常具有高稀疏性）的矩阵乘法。SSR 引入了专用的三元数据格式和利用计算树稀疏性模式的算法，与 RSR++ 等现有方法相比，在理论和实践上都实现了加速。 AI

影响可能有助于在计算资源有限的硬件上更有效地部署 LLM。

排序理由详细介绍加速 LLM 推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Adeline Pittet, Shien Zhu, Val\'erie Verdan, Gustavo Alonso · 2026-10-07 04:00

SSR：稀疏分段缩减用于三元GEMM加速

arXiv:2610.08403v1 Announce Type: new Abstract: Large Language Models (LLMs) require substantial computational resources, limiting their deployment on resource-constrained hardware. Ternary LLMs mitigate these demands through weight quantization via ternary values, achieving sign…