Astrocyte-Integrated Dynamic Function Exchange in Spiking Neural Networks

TL;DR

This paper introduces astrocyte-augmented Spiking Neural Networks with real-time hardware reconfiguration, significantly improving robustness and computational efficiency in neuromorphic systems.

Contribution

It presents a novel integration of astrocytes into SNNs and implements DFX technology on FPGA for adaptive, real-time reconfiguration.

Findings

Enhanced fault tolerance in SNNs

Near-zero latency and infinite throughput in implementation

Surpassed prior models in neuron and synapse efficiency

Abstract

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.