FAS converts pretrained LLMs to spiking LLMs by fine-tuning with QCFS and then calibrating thresholds and initial membrane potentials, reaching near-LLM accuracy at 8-16 timesteps with large claimed energy savings.
Spiking deep convolutional neural networks for energy-efficient object recognition
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
FAS: Fast ANN-SNN Conversion for Spiking Large Language Models
FAS converts pretrained LLMs to spiking LLMs by fine-tuning with QCFS and then calibrating thresholds and initial membrane potentials, reaching near-LLM accuracy at 8-16 timesteps with large claimed energy savings.