  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.
