SH-WRNN：面向非对称边缘智能的隐式球谐权重场路由神经网络
SH-WRNN: Implicit Spherical Harmonics Weight Field Routing Neural Networks for Asymmetric Edge Intelligence
AI总结：
SH-WRNN将权重矩阵参数化为球谐连续场，通过经纬度路由动态提取权重，实现单周期高精度训练，并利用表面烘焙实现非对称加速，为边缘智能提供新范式。
AI中文摘要：
深度学习架构仍然僵化地建立在传统的全连接层之上。尽管网络规模不断扩大，但很少有人挑战这一根本基础。在这项工作中，我们通过将核心突触权重矩阵从静态的离散参数转变为由球谐函数控制的可微连续场，重塑了这一范式。我们引入了隐式球谐权重场路由神经网络（SH-WRNN），该网络将权重矩阵约束在一个连续的参数场内，而不是优化数百万个局部离散权重。当检索当前层的权重矩阵时，连接参数通过从连续场映射的矩形平面上的经纬度进行定位。纬度坐标由前一层的激活神经元指定，而经度坐标则由通过矩阵乘法从前一层激活生成的键决定。通过评估该映射上的交点，网络动态地即时提取其连接权重。在MNIST上的实证验证表明，在紧凑配置（32, 10, 10）和（32, 3, 10）下，SH-WRNN在单个训练周期内分别达到了91.05%和81.54%的稳健准确率。此外，我们提出了一种非对称的表面烘焙方案。收敛后，连续权重场被一次性烘焙成静态参数化表面。通过在推理过程中消除解析球谐计算，并将动态矩阵提取简化为高速局部内存切片，该方案实现了非对称算法加速，且精度下降可忽略不计。这种范式转变绕过了GPU内存带宽的垄断，开辟了一条重塑CPU计算优势的新途径。代码可在以下网址获取：此https URL。
英文摘要：
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 matrix from static, discrete parameters into a differentiable, continuous field governed by spherical harmonics functions. We introduce the Implicit Spherical Harmonics Weight Field Routing Neural Network (SH-WRNN), which constrains weight matrices within a continuous parametric field instead of optimizing millions of localized discrete weights. When retrieving the weight matrix of the current layer, connection parameters are localized using latitude and longitude on a rectangular plane mapped from the continuous field. The latitudinal coordinate is specified by activated neurons from the previous layer, while the longitudinal coordinate is determined by keys generated from previous layer activations via matrix multiplication. By evaluating intersections on this map, the network dynamically extracts its connection weights on-the-fly. Empirical validation on MNIST demonstrates that under compact configurations of (32, 10, 10) and (32, 3, 10), SH-WRNN achieves robust accuracies of 91.05% and 81.54% within a single training epoch. Furthermore, we propose an asymmetric Surface Baking scheme. Upon convergence, the continuous weight field is baked once into a static parametric surface. By eliminating analytical spherical harmonics calculations during inference and reducing dynamic matrix extraction to high-speed localized memory slicing, this scheme achieves asymmetric algorithmic acceleration with negligible accuracy degradation. This paradigm shift bypasses GPU memory-bandwidth monopolies, opening a novel path to reshape the advantages of CPU computing. Code is available at https://github.com/jzb1111/SphericalHarmonyRoutedNeuralNetWork.
