Introduction
Spiking Neural Networks (SNNs) represent a major shift from traditional artificial neural networks, offering energy-efficient computing for real-time applications. Their event-based processing mimics biological neurons, making them highly suitable for resource-constrained environments such as robotics and vision systems.
SNN in Vision Systems
SNNs process data in spikes, as opposed to traditional continuous activations. This event-driven mechanism only activates neurons when a specific threshold is reached, conserving energy. In vision systems, this is particularly useful, as the workload can be distributed based on the importance of visual inputs.
The challenge lies in efficiently training SNNs to perform well under low-latency conditions. Traditional backpropagation methods used in ANNs need to be adapted to handle spike-based learning. Novel techniques like spike-timing-dependent plasticity (STDP) are being explored to enable SNNs to learn more naturally.
Future Work
Future directions include integrating SNNs with edge AI systems, allowing real-time decision-making in applications such as autonomous vehicles and drones. Further exploration will involve training techniques that can balance energy efficiency with performance improvements in large-scale deployments.