SpikingLM: Towards Fully Spiking Language Model

Leveraging event-driven computation mechanism, Spiking Neural Networks (SNNs) have emerged as a representative paradigm for energy-efficient edge intelligence. However, extending SNNs to modern deep language models still faces two fundamental challenges. First, dead neurons in deep SNNs lead to degraded gradients, limiting the training effectiveness of spiking language models. Second, removing Softmax for energy efficiency weakens token-wise competition, reducing the model’s ability to select salient tokens. To address these challenges, we propose Spiking Language Model (SpikingLM) to bridge the efficiency of SNNs and the capability of modern language models through two key innovations. First, we propose Distribution-aware Scaling method, which rescales linear outputs into an activation-friendly range to alleviate dead neurons and stabilize gradient propagation. Notably, its scaling parameters can be fused into the preceding linear layers, incurring no additional inference overhead. Second, we introduce Spike2Max to restore winner-takes-all mechanism via base-2 exponentiation and max-subtraction. Compared with Softmax, Spike2Max reduces energy consumption by over 95% using hardware-efficient bit-shift operations. Experiments show that SpikingLM reduces energy consumption by 57.9% and achieves state-of-the-art performance on GLUE, laying a promising foundation for energy-efficient language modeling. Code is available at https://github.com/hamings1/SpikingLM.git.