Spiking Neural Networks: The Future of Brain-Inspired Computing

TL;DR

This paper provides a comprehensive analysis of Spiking Neural Networks (SNNs), highlighting their models, training methods, and performance metrics, demonstrating their potential for energy-efficient, low-latency, and adaptive computing applications.

Contribution

It offers an in-depth comparison of SNN training strategies, including surrogate gradient, conversion, and STDP, and evaluates their performance and energy efficiency.

Findings

Surrogate gradient-trained SNNs achieve near-ANN accuracy within 1-2%.

Converted SNNs perform competitively but need longer simulation times.

STDP-based SNNs have the lowest energy consumption and spike counts.

Abstract

Spiking Neural Networks (SNNs) represent the latest generation of neural computation, offering a brain-inspired alternative to conventional Artificial Neural Networks (ANNs). Unlike ANNs, which depend on continuous-valued signals, SNNs operate using distinct spike events, making them inherently more energy-efficient and temporally dynamic. This study presents a comprehensive analysis of SNN design models, training algorithms, and multi-dimensional performance metrics, including accuracy, energy consumption, latency, spike count, and convergence behavior. Key neuron models such as the Leaky Integrate-and-Fire (LIF) and training strategies, including surrogate gradient descent, ANN-to-SNN conversion, and Spike-Timing Dependent Plasticity (STDP), are examined in depth. Results show that surrogate gradient-trained SNNs closely approximate ANN accuracy (within 1-2%), with faster convergence by the 20th epoch and latency as low as 10 milliseconds. Converted SNNs also achieve competitive performance but require higher spike counts and longer simulation windows. STDP-based SNNs, though slower to converge, exhibit the lowest spike counts and energy consumption (as low as 5 millijoules per inference), making them optimal for unsupervised and low-power tasks. These findings reinforce the suitability of SNNs for energy-constrained, latency-sensitive, and adaptive applications such as robotics, neuromorphic vision, and edge AI systems. While promising, challenges persist in hardware standardization and scalable training. This study concludes that SNNs, with further refinement, are poised to propel the next phase of neuromorphic computing.