用神经形态芯片实现高效低功耗的多维谐波提取，性能损失小、功耗降五倍。
Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware
- 将谐波提取转为稀疏恢复问题，用深度展开的CNN求解
- 复杂值CNN经转换后在神经形态芯片上运行，功耗降低近5倍
- 适合追求低功耗的实时信号处理场景，如便携式雷达
本文探索基于转换的神经形态算法在高精度、低功耗单快照多维谐波提取（MHR）中的潜力。通过将MHR问题建模为稀疏恢复问题，提出一种基于深度展开的结构化学习迭代收缩阈值（S-LISTA）算法，采用具有复数激活的卷积神经网络（CNN）求解，并以监督回归方式训练。随后，提出一种新方法将复数卷积层与激活转换为脉冲神经网络（SNN），核心是改进的少量脉冲（FS）转换，通过调整神经元模型参数和内部动态，处理复数计算中实部与虚部的固有耦合。最终，转换后的SNN部署于SpiNNaker2神经形态板，与部署在NVIDIA Jetson Xavier上的原始CNN对比。测量结果显示，转换后的SNN在中等性能损失下实现接近五倍的能效提升。
原文摘要 · Abstract (English)
This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval (MHR). By casting the MHR problem as a sparse recovery problem, we devise the currently proposed, deep-unrolling-based Structured Learned Iterative Shrinkage and Thresholding (S-LISTA) algorithm to solve it efficiently using complex-valued convolutional neural networks with complex-valued activations, which are trained using a supervised regression objective. Afterward, a novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. At the heart of this method lies the recently proposed Few Spikes (FS) conversion, which is extended by modifying the neuron model's parameters and internal dynamics to account for the inherent coupling between real and imaginary parts in complex-valued computations. Finally, the converted SNNs are mapped onto the SpiNNaker2 neuromorphic board, and a comparison in terms of estimation accuracy and power efficiency between the original CNNs deployed on an NVIDIA Jetson Xavier and the SNNs is being conducted. The measurement results show that the converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
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