Why do spiking neural networks demonstrate superior energy efficiency compared to traditional backpropagation-based deep learning models?

Spiking neural networks achieve superior energy efficiency primarily through sparse, event-driven computation. In traditional deep learning models, neurons are represented by continuous numerical values that are updated and processed during every single operation, regardless of whether the input signal is meaningful. This requires the constant use of power-hungry matrix multiplications across the entire network. In contrast, spiking neural networks mimic bio....