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Achieving 5.6x better energy efficiency in SNNs by decoupling synaptic and neuronal computations could redefine the deployment of brain-inspired models in real-world applications.
ReSCom achieves unprecedented energy efficiency in spiking neural networks, consuming just 0.05 mJ per image while maintaining high accuracy.