Low Latency Spiking Neural Network

Low-latency spiking neural networks (SNNs) aim to achieve fast and energy-efficient inference by mimicking the brain's event-driven computation. Current research focuses on improving the accuracy of these networks, particularly through novel training algorithms like backpropagation with differentiable spike representations and optimized initialization techniques, as well as exploring alternative architectures beyond convolutional neural networks, such as Transformers. These advancements address the challenge of achieving high accuracy with minimal time steps, leading to significant potential for applications in resource-constrained devices and real-time processing.