这篇论文把S4模型剪掉70%计算量还能保持性能，想在小设备上跑S4模型可以看看。
论文提出S4oP，一种增量式运算符级剪枝方法，针对S4和S4D结构状态空间模型。该方法通过交替结构化掩码和微调逐步剪枝运算符。实验在多个基准数据集上表明，剪枝70%的模型运算符仍能保持原模型性能，同时显著降低推理延迟。这是首次系统研究SSM的结构化运算符剪枝。
S4oP: Operator-level Pruning of Structured State Space Models for Resource-Constrained Devices
Structured State Space Models (SSMs), including the S4 and S4D architectures, have recently emerged as powerful alternatives to attention-based models for capturing long-range dependencies in sequential data. Despite their strong empirical performance, deploying these models in time- and resource-constrained settings remains challenging due to their computational and memory demands. In this paper, we propose a novel incremental, operator-level pruning approach for S4- and S4D-based models that significantly reduces inference cost while preserving predictive performance. To the best of our knowledge, this is the first work to systematically investigate structured operator pruning for SSMs. Our method progressively prunes model operators by interleaving structured masking with fine-tuning, while jointly monitoring accuracy and inference latency. We implement this approach within a unified training and evaluation framework that enables systematic exploration of efficiency-accuracy trade-offs. Experiments across multiple benchmark datasets show that pruning up to 70% of the model operators preserves the performance of the original models in most cases, while substantially reducing inference latency. These results demonstrate that structured operator pruning is an effective and previously unexplored strategy for improving the efficiency of SSMs and facilitate their deployment in practical, resource-constrained scenarios.