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.

翻译：本文探讨了基于转换的神经形态算法在实现高精度、高能效的单快照多维谐波恢复（MHR）方面的潜力。通过将MHR问题构建为稀疏恢复问题，我们设计了当前提出的、基于深度展开的结构化学习迭代收缩阈值（S-LISTA）算法，该算法利用具有复值激活函数的复值卷积神经网络，通过监督回归目标进行训练，从而高效求解该问题。随后，我们开发了一种将复值卷积层和激活函数转换为脉冲神经网络（SNN）的新方法。该方法的核心是最近提出的Few Spikes（FS）转换，我们通过修改神经元模型的参数和内部动力学来扩展该方法，以考虑复值计算中实部和虚部之间固有的耦合关系。最后，将转换后的SNN映射到SpiNNaker2神经形态板上，并与部署在NVIDIA Jetson Xavier上的原始CNN在估计精度和能效方面进行比较。测量结果表明，与原始CNN相比，转换后的SNN在性能损失适中的情况下，实现了近五倍的能效提升。