An Analytical Estimation of Spiking Neural Networks Energy Efficiency

Abstract

Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks, but few works have proven these claims to be true. In this work, we present a metric to estimate the energy consumption of SNNs independently of a specific hardware. We then apply this metric on SNNs processing three different data types (static, dynamic and event-based) representative of real-world applications. As a result, all of our SNNs are 6 to 8 times more efficient than their FNN counterparts.

Accepted for ICONIP 2022 Conference

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Technical report: supervised training of convolutional spiking neural networks with PyTorch
• Spiking Neural Networks for Frame-based and Event-based Single Object Localization
• NxTF: An API and Compiler for Deep Spiking Neural Networks on Intel Loihi

Cited by in corpus (3)

• Neuromorphic Event-Driven Semantic Communication in Microgrids
• Energy efficiency analysis of Spiking Neural Networks for space applications
• Energy Aware Development of Neuromorphic Implantables: From Metrics to Action