Radio astronomy relies on bespoke, experimental and innovative computing solutions. This will continue as next-generation telescopes such as the Square Kilometre Array (SKA) and next-generation Very Large Array (ngVLA) take shape. Under increasingly demanding power consumption, and increasingly challenging radio environments, science goals may become intractable with conventional von Neumann computing due to related power requirements. Neuromorphic computing offers a compelling alternative, and combined with a desire for data-driven methods, Spiking Neural Networks (SNNs) are a promising real-time power-efficient alternative. Radio Frequency Interference (RFI) detection is an attractive use-case for SNNs where recent exploration holds promise. This work presents a comprehensive analysis of the potential impact of deploying varying neuromorphic approaches across key stages in radio astronomy processing pipelines for several existing and near-term instruments. Our analysis paves a realistic path from near-term FPGA deployment of SNNs in existing instruments, allowing the addition of advanced data-driven RFI detection for no capital cost, to neuromorphic ASICs for future instruments, finding that commercially available solutions could reduce the power budget for key processing elements by up to three orders of magnitude, transforming the operational budget of the observatory. High-data-rate spectrographic processing could be a well-suited target for the neuromorphic computing industry, as we cast radio telescopes as the world's largest in-sensor compute challenge.

翻译：射电天文学依赖于定制化、实验性和创新性的计算解决方案。随着下一代望远镜如平方公里阵列（SKA）和下一代甚大阵列（ngVLA）的成型，这一趋势将持续。在日益严苛的功耗要求和日益复杂的射电环境下，传统冯·诺依曼计算因其相关的功耗需求可能使科学目标难以实现。神经形态计算提供了一个引人注目的替代方案，结合对数据驱动方法的需求，脉冲神经网络（SNNs）成为一种有前景的实时高能效替代方案。射频干扰（RFI）检测是SNNs一个有吸引力的应用场景，近期的探索已显示出潜力。本研究全面分析了在多个现有及近期仪器中，将不同神经形态方法部署于射电天文处理流程关键阶段的潜在影响。我们的分析描绘了一条从近期在现有仪器中基于FPGA部署SNNs（允许以零资本成本增加先进的基于数据驱动的RFI检测功能）到为未来仪器采用神经形态专用集成电路的现实路径，发现商用解决方案可将关键处理单元的功耗预算降低多达三个数量级，从而改变天文台的运行预算。高数据率光谱处理可能是神经形态计算产业一个非常适合的目标，因为我们将射电望远镜定位为全球最大的传感器内计算挑战。