标题： mGRADE：最小循环门控与延迟卷积用于轻量级序列建模 显示英文标题

标题： mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling

摘要： 边缘设备用于时间处理需要在严格的内存约束下捕捉短期和长期动态的模型。 虽然Transformer在序列建模方面表现出色，但它们的内存消耗随序列长度呈二次增长，这使得它们在这些场景中不切实际。 循环神经网络（RNNs）具有恒定的内存，但训练是顺序进行的，而时间卷积网络（TCNs）虽然高效，但内存消耗随着内核大小增加。 为了解决这个问题，我们提出了mGRADE（带有延迟嵌入的最小门控循环架构），这是一种混合内存系统，结合了带有可学习间隔的时间1D卷积，随后是一个最小门控循环单元（minGRU）。 这种设计使卷积层能够实现灵活的延迟嵌入，以捕捉快速的时间变化，同时循环模块以最小的内存开销有效地保持全局上下文。 我们在两个合成任务上验证了我们的方法，结果表明mGRADE能够有效分离并保留多尺度的时间特征。 此外，在具有挑战性的逐像素图像分类基准测试中，mGRADE在使用大约20%更少的内存占用的情况下，始终优于纯卷积和纯循环模型，突显了其在边缘设备上内存受限的时间处理中的适用性。 这突显了mGRADE作为边缘设备上内存受限的多尺度时间处理的高效解决方案的潜力。 显示英文摘要

摘要： Edge devices for temporal processing demand models that capture both short- and long- range dynamics under tight memory constraints. While Transformers excel at sequence modeling, their quadratic memory scaling with sequence length makes them impractical for such settings. Recurrent Neural Networks (RNNs) offer constant memory but train sequentially, and Temporal Convolutional Networks (TCNs), though efficient, scale memory with kernel size. To address this, we propose mGRADE (mininally Gated Recurrent Architecture with Delay Embedding), a hybrid-memory system that integrates a temporal 1D-convolution with learnable spacings followed by a minimal gated recurrent unit (minGRU). This design allows the convolutional layer to realize a flexible delay embedding that captures rapid temporal variations, while the recurrent module efficiently maintains global context with minimal memory overhead. We validate our approach on two synthetic tasks, demonstrating that mGRADE effectively separates and preserves multi-scale temporal features. Furthermore, on challenging pixel-by-pixel image classification benchmarks, mGRADE consistently outperforms both pure convolutional and pure recurrent counterparts using approximately 20% less memory footprint, highlighting its suitability for memory-constrained temporal processing at the edge. This highlights mGRADE's promise as an efficient solution for memory-constrained multi-scale temporal processing at the edge.