SNN Conversion

ANN-SNN conversion focuses on efficiently transforming the weights and activations of trained artificial neural networks (ANNs) into spiking neural networks (SNNs), leveraging ANNs' training efficiency while harnessing SNNs' energy efficiency and suitability for neuromorphic hardware. Current research emphasizes minimizing conversion errors and reducing inference latency, often employing novel neuron models (e.g., group neurons, consistent IF neurons) and optimization strategies targeting residual membrane potentials or temporal biases. These advancements aim to bridge the performance gap between ANNs and SNNs, paving the way for more energy-efficient and faster deep learning applications in resource-constrained environments.