This research introduces Fast ANN-SNN Conversion (FAS), a novel strategy that efficiently transforms traditional Large Language Models into energy-efficient Spiking Neural Networks while preserving performance.

• Uses a two-stage conversion process with full-parameter fine-tuning of pre-trained models
• Applies specialized calibration methods to minimize conversion errors
• Achieves better performance than existing conversion approaches with lower computational overhead
• Demonstrates practical viability for energy-efficient AI deployment

This engineering advancement matters because it makes neuromorphic computing more practical for large-scale language tasks, potentially enabling more sustainable AI deployments in resource-constrained environments.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models