This paper presents an innovative methodology for improving the robustness and computational efficiency of Spiking Neural Networks (SNNs), a critical component in neuromorphic computing. The proposed approach integrates astrocytes, a type of glial cell prevalent in the human brain, into SNNs, creating astrocyte-augmented networks. To achieve this, we designed and implemented an astrocyte model in two distinct platforms: CPU/GPU and FPGA. Our FPGA implementation notably utilizes Dynamic Function Exchange (DFX) technology, enabling real-time hardware reconfiguration and adaptive model creation based on current operating conditions. The novel approach of leveraging astrocytes significantly improves the fault tolerance of SNNs, thereby enhancing their robustness. Notably, our astrocyte-augmented SNN displays near-zero latency and theoretically infinite throughput, implying exceptional computational efficiency. Through comprehensive comparative analysis with prior works, it's established that our model surpasses others in terms of neuron and synapse count while maintaining an efficient power consumption profile. These results underscore the potential of our methodology in shaping the future of neuromorphic computing, by providing robust and energy-efficient systems.

翻译：本文提出了一种创新方法，旨在提升脉冲神经网络（SNNs）的鲁棒性与计算效率——脉冲神经网络是神经形态计算的关键组成部分。该方法将星形胶质细胞（人脑中普遍存在的一种胶质细胞）整合到SNNs中，构建星形胶质细胞增强型网络。为实现这一目标，我们在CPU/GPU与FPGA两种不同的平台上设计并实现了星形胶质细胞模型。其中，FPGA实现方案创新性地采用了动态功能互换技术，支持基于当前运行条件进行实时硬件重配置与自适应模型构建。这种利用星形胶质细胞的新颖方法显著提升了SNNs的容错能力，从而增强了其鲁棒性。值得注意的是，我们开发的星形胶质细胞增强型SNN展现出近乎零延迟与理论上的无限吞吐量，体现了卓越的计算效率。通过与以往研究进行全面的对比分析，证实我们的模型在神经元与突触数量上均优于其他模型，同时保持了高效的功耗特性。这些结果充分彰显了该方法通过提供鲁棒且节能的系统，在塑造神经形态计算未来发展方向上的巨大潜力。