English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents an innovative methodology for improving the robustnessand computational efficiency of Spiking Neural Networks (SNNs), a criticalcomponent in neuromorphic computing. The proposed approach integratesastrocytes, a type of glial cell prevalent in the human brain, into SNNs,creating astrocyte-augmented networks. To achieve this, we designed andimplemented an astrocyte model in two distinct platforms: CPU/GPU and FPGA. OurFPGA implementation notably utilizes Dynamic Function Exchange (DFX)technology, enabling real-time hardware reconfiguration and adaptive modelcreation based on current operating conditions. The novel approach ofleveraging astrocytes significantly improves the fault tolerance of SNNs,thereby enhancing their robustness. Notably, our astrocyte-augmented SNNdisplays near-zero latency and theoretically infinite throughput, implyingexceptional computational efficiency. Through comprehensive comparativeanalysis with prior works, it's established that our model surpasses others interms of neuron and synapse count while maintaining an efficient powerconsumption profile. These results underscore the potential of our methodologyin shaping the future of neuromorphic computing, by providing robust andenergy-efficient systems.

Astrocyte-Integrated Dynamic Function Exchange in Spiking Neural Networks