Despite remarkable improvements in speed and accuracy, convolutional neural networks (CNNs) still typically operate as monolithic entities at inference time. This poses a challenge for resource-constrained practical applications, where both computational budgets and performance needs can vary with the situation. To address these constraints, we propose the Any-Width Network (AWN), an adjustable-width CNN architecture and associated training routine that allow for fine-grained control over speed and accuracy during inference. Our key innovation is the use of lower-triangular weight matrices which explicitly address width-varying batch statistics while being naturally suited for multi-width operations. We also show that this design facilitates an efficient training routine based on random width sampling. We empirically demonstrate that our proposed AWNs compare favorably to existing methods while providing maximally granular control during inference.

本文提出了一种可调节宽度的CNN架构和相关训练程序——Any-Width Network（AWN）——它允许在推理过程中对速度和准确性进行精细控制，其关键创新是使用下三角权重矩阵，该矩阵可以明确地解决宽度变化批量统计问题，并且自然适合于多宽度操作，同时我们也证明了这种设计有助于基于随机宽度抽样的高效训练方案，我们通过实验证明，我们提出的AWN在比较现有方法时具有优势，同时在推理过程中提供了最细粒度的控制。

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