🤖 AI Summary
Traditional artificial neural networks (ANNs) suffer from low energy efficiency and slow dynamic response, limiting their suitability for edge and neuromorphic computing. Method: This study systematically investigates modeling and training mechanisms of leaky integrate-and-fire (LIF) spiking neural networks (SNNs) for brain-inspired computing. We propose a multi-strategy comparative framework integrating surrogate gradient descent, ANN-to-SNN conversion, and spike-timing-dependent plasticity (STDP), evaluated across accuracy, energy consumption (mJ/inference), latency (ms), and convergence epochs. Results: Surrogate gradient training achieves near-ANN accuracy (within 1–2% degradation), converges by epoch 20, and attains inference latency as low as 10 ms; STDP yields ultra-low energy consumption (5 mJ/inference), enabling unsupervised learning and ultra-low-power edge deployment. This work quantitatively characterizes, for the first time, the fundamental trade-offs among accuracy, energy efficiency, and latency across SNN training paradigms—providing a deployable optimization roadmap for robotic perception, neuromorphic vision, and edge AI.
📝 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.