事件驱动的神经形态压缩感知信号重建
Event-driven signal reconstruction through neuromorphic compressive sensing
- Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对传统压缩感知成本受系统维度限制的问题，提出以S-LISTA为核心的神经形态压缩感知框架，通过稀疏事件表示实现资源节省，在保持性能的同时显著降低传输与重建能耗。
AI中文摘要：
压缩感知（CS）利用信号固有的稀疏性，实现对高维数据的高效表示。然而，传统压缩感知依赖于固定长度的测量表示和密集的重建操作，导致通信和计算成本主要由系统维度决定，而非信号稀疏性。在此，我们提出了一种以脉冲驱动的学习型迭代收缩阈值算法（S-LISTA）为核心的神经形态压缩感知框架。通过将压缩测量和重建更新表示为稀疏事件，该框架将信号固有的稀疏性与脉冲神经网络（SNN）的时空稀疏性联系起来，将稀疏性的优势扩展到感知、传输和重建的全过程。因此，通信和计算成本取决于事件活动，使得稀疏表示能够转化为资源节省。大量实验表明，在保持有竞争力的重建和下游任务性能的同时，所传输的数据量和估计的重建能量显著减少。该框架在具有挑战性的信道条件下也表现出鲁棒性。这些发现确立了神经形态压缩感知作为资源受限场景下信号重建的一种有前景的范式。
英文摘要：
Compressive sensing (CS) exploits intrinsic signal sparsity for efficient representation of high-dimensional data. However, conventional CS relies on fixed-length measurement representations and dense reconstruction operations, leaving communication and computational costs largely determined by system dimensions rather than signal sparsity. Here, we propose a neuromorphic CS framework centred on a spike-driven learned iterative shrinkage-thresholding algorithm (S-LISTA). By representing compressed measurements and reconstruction updates as sparse events, the framework links intrinsic signal sparsity to the spatiotemporal sparsity of spiking neural networks (SNNs), extending the benefits of sparsity across sensing, transmission and reconstruction. Communication and computational costs consequently depend on event activity, allowing sparse representations to translate into resource savings. Extensive experiments demonstrate substantial reductions in transmitted data volume and estimated reconstruction energy while maintaining competitive reconstruction and downstream task performance. The framework also exhibits robustness under challenging channel conditions. These findings establish neuromorphic CS as a promising paradigm for signal reconstruction in resource-constrained scenarios.
