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Astrocyte-Integrated Dynamic Function Exchange in Spiking Neural Networks

EasyChair Preprint no. 10953

10 pagesDate: September 23, 2023


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.

Keyphrases: Astrocytes, Dynamic Function Exchange, fault tolerance, FPGA implementation, Spiking Neural Networks

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Murat Isik and Kayode Inadagbo},
  title = {Astrocyte-Integrated Dynamic Function Exchange in Spiking Neural Networks},
  howpublished = {EasyChair Preprint no. 10953},

  year = {EasyChair, 2023}}
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