English(EN) Gram-Space: Structure-Preserving Codebook Compression for Memory-Efficient Neuro-Symbolic AI

Gram-Space框架大幅降低神经符号AI的内存使用

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-04 04:00

研究人员推出Gram-Space，一个旨在解决神经符号AI内存瓶颈的新型压缩框架。该框架利用Gram-Schmidt过程，在一个紧凑的正交坐标系中表示码本向量，为VSA算子保留了重要的点积结构。在神经符号推理数据集上的基准测试表明，Gram-Space可以将GPU内存使用量显著降低高达15.75倍，并将推理延迟提高高达3.62倍，从而提高这些工作负载的硬件利用率。 AI

影响降低神经符号AI的内存和延迟，可能支持更大、更复杂的模型。

排序理由详细介绍AI效率新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Weilun Wang, Wantong Li · 2026-08-04 04:00

Gram-Space：用于内存高效神经符号AI的结构保持码本压缩

arXiv:2608.01528v1 Announce Type: new Abstract: Vector symbolic architectures (VSA) are widely used for reasoning in neuro-symbolic (NeSy) AI, yet high-dimensional codebooks often create severe memory bottlenecks that limit scalability and deployment. In this paper, we propose Gr…

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Gram-Space：用于内存高效神经符号AI的结构保持码本压缩

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