Large Language Models with At Most One Spike per Neuron
Leveraging their inherent sparse event-driven computation, spiking neural networks (SNNs) offer a promising path toward energy-efficient large language models (LLMs). Time-to-first-spike (TTFS) coding generates at most one spike per neuron within a time window, yielding extremely low firing rates. However, conventional TTFS SNNs are restricted to specific structures, making it challenging to encode certain blocks in LLM -- such as layer normalization and matrix multiplication --using TTFS. To overcome this limitation, we introduce a reference-based strategy specifically to encode the four core LLM components: embedding layers, layer normalization, attention-related operations and dropout....
| Authors | Zhuoya Zhao, Parsa Omidi, Aref Jafari et al. |
|---|---|
| Primary category | cs.NE |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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