Analyzing The Efficiency of Spiking Neural Networks in Real -Time Edge Computing Applications
Ragipani Sowmya,
Bushra Muneeb,
Yerraginnela Shravani
This study investigates the efficiency of Spiking Neural Networks (SNNs) compared to traditional neural
network architectures—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs),
and Fully Connected Neural Networks (FCNNs)—in real-time edge computing applications. Through
experimental evaluation, we examine key performance metrics including latency, energy consumption,
accuracy, and computational complexity. Our results indicate that SNNs exhibit superior performance
in terms of latency and energy efficiency, with an average latency of 15 milliseconds and energy
consumption of 2.5 millijoules, significantly outperforming CNNs, RNNs, and FCNNs. While SNNs show
slightly lower accuracy (85%) compared to CNNs (90%), they require fewer computational resources,
with a total of 1.2 × 10^9 floating-point operations (FLOPs), making them particularly suitable for
power-constrained edge devices. This study highlights the potential of SNNs to address the stringent
requirements of real-time edge computing while offering insights into the trade-offs between efficiency
and accuracy.
