Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resource-constrained devices still proved challenging. Therefore, we present an early-exiting model based on nsNet2 that provides several levels of accuracy and resource savings by halting computations at different stages. Moreover, we adapt the original architecture by splitting the information flow to take into account the injected dynamism. We show the trade-offs between performance and computational complexity based on established metrics.

翻译：尽管深度学习在深度噪声抑制领域取得了进展，但在资源受限设备上利用深层架构仍具有挑战性。因此，我们提出了一种基于nsNet2的早退模型，该模型通过在不同阶段终止计算，在多个准确率等级与资源节约之间实现平衡。此外，我们通过分割信息流对原始架构进行调整，以适配注入的动态性。基于既定指标，我们展示了性能与计算复杂度之间的权衡。