标题： 基于$H^2$最优缩减的深度对角状态空间模型压缩方法 显示英文标题

标题： Compression Method for Deep Diagonal State Space Model Based on $H^2$ Optimal Reduction

摘要： 深度学习模型结合线性状态空间模型（SSM）已被关注用于捕捉序列数据中的长程依赖关系。 然而，它们较大的参数规模在资源受限设备上的部署带来了挑战。 在本研究中，我们通过将控制理论中的$H^{2}$模型降阶技术应用于其线性 SSM 组件，提出了一种高效的参数减少方法。 在实验中，LRA 基准结果表明，基于我们提出的方法的模型压缩优于使用平衡截断的现有方法，同时成功将 SSM 中的参数数量减少到$1/32$而不牺牲原始模型的性能。 显示英文摘要

摘要： Deep learning models incorporating linear SSMs have gained attention for capturing long-range dependencies in sequential data. However, their large parameter sizes pose challenges for deployment on resource-constrained devices. In this study, we propose an efficient parameter reduction method for these models by applying $H^{2}$ model order reduction techniques from control theory to their linear SSM components. In experiments, the LRA benchmark results show that the model compression based on our proposed method outperforms an existing method using the Balanced Truncation, while successfully reducing the number of parameters in the SSMs to $1/32$ without sacrificing the performance of the original models.