发表机构
The George Washington University（乔治·华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大型AI模型的内存瓶颈，提出线性可复用神经基架构，通过共享神经基的线性组合实现高压缩率，实验显示其收敛更快、损失更低且训练稳定。
AI 中文摘要
随着大型人工智能模型在各类应用中愈发普及，内存成本已成为训练与推理环节的关键瓶颈。为缓解该问题，我们提出线性可复用神经基架构（LRNBA），这一新型框架旨在提升参数效率、降低内存成本。受循环神经网络（RNN）设计启发，我们方法的核心思路是将每个网络块表示为一组共享神经基的线性组合，从而在实现极高网络压缩率的同时保持训练稳定性。该架构允许在相同参数预算下构建显著更宽、更深的网络。大量实验表明，我们的模型相比经典架构，能达到相当甚至更快的收敛速度与更低的损失值，同时维持稳定的训练动态。
英文摘要
Memory constraints remain a critical bottleneck in the deployment of large-scale AI models. Parameter sharing across network depth reduces model storage, but repeatedly applying an identical transformation limits flexibility across layers. Inspired by time--memory trade-offs in classical algorithms, we introduce the Linear Reusable Neural Bases (LRNB) architecture, an RNN-based framework that improves parameter efficiency through parameter reuse at the \textit{neuron level}. Each feedforward residual module is represented as a linear combination of shared neural bases, with depth-specific learnable coefficients and optional shifts providing flexibility across layers. This formulation reduces parameter redundancy across depth and enables the construction of wider and deeper networks within a fixed parameter budget. We further provide a geometric interpretation of the neural bases from a vector-field perspective and extend the framework to linear projection modules. Experiments demonstrate that the LRNB architecture achieves comparable or lower final training loss than independently parameterized baselines while using fewer parameters and maintaining stable training dynamics. These findings support neuron-level reuse as a practical approach to parameter-efficient network design.
