English(EN) SH-WRNN: Implicit Spherical Harmonics Weight Field Routing Neural Networks for Asymmetric Edge Intelligence

新型SH-WRNN模型使用球谐函数处理神经网络权重

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员介绍了一种新颖的神经网络架构，称为SH-WRNN，它用由球谐函数定义的连续场替换静态权重矩阵。这种方法允许网络在操作过程中动态提取连接权重，可能减少对大型离散参数优化的依赖。SH-WRNN在单个训练周期内展示了在MNIST数据集上的高准确率。此外，还提出了一种“表面烘焙”方案，将连续场转换为静态参数表面以进行推理，从而实现不对称算法加速，并通过绕过GPU内存带宽限制，可能将优势转移到CPU计算上。 AI

影响引入了一种新颖的神经网络权重表示方法，可能影响模型效率和硬件利用率。

排序理由描述新颖神经网络架构和技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zhibin Jiao, Xiangjing An · 2026-09-15 04:00

SH-WRNN：用于不对称边缘智能的隐式球谐权重场路由神经网络

arXiv:2609.14614v1 Announce Type: new Abstract: Deep learning architectures remain rigidly built upon traditional fully connected layers. While networks scale up, few challenge this foundational root. In this work, we reshape this paradigm by transforming the core synapse weight …

本文如何被排名

报道来源 [1]

SH-WRNN：用于不对称边缘智能的隐式球谐权重场路由神经网络

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