<ul><li>Spiking Large Language Models (LLMs) offer an energy-efficient alternative to conventional LLMs through event-driven computation.</li><li>Researchers have been developing ANN-to-SNN conversion methods to create spiking LLMs while maintaining energy efficiency, but face challenges with extreme activation outliers and incompatible operations.</li><li>A new approach called Loss-less ANN-SNN Conversion for Fully Spike-Driven LLMs (LAS) is proposed to address these challenges by introducing novel neurons to handle activation outliers and nonlinear operations.</li><li>Experimental results show that LAS achieves loss-less conversion and even improves accuracy on tasks like the WSC task. Source code for LAS is available at https://github.com/lc783/LAS.</li></ul>

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models

Discover more