Large Language Models with At Most One Spike per Neuron

Zhuoya Zhao; Parsa Omidi; Aref Jafari; Richard Naud

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arXiv · 2609.05151

Large Language Models with At Most One Spike per Neuron

Zhuoya Zhao·Parsa Omidi·Aref Jafari·Richard Naud

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Abstract

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. We construct a fully TTFS-based SNN architecture and train it end-to-end. Experiments on modern LLMs like BERT and GPT-2 demonstrate that our approach achieves performance comparable to ANN counterparts on natural language understanding and common-sense reasoning, while a clear gap remains on language modeling perplexity. To the best of our knowledge, this is the first work to scale a spiking LLM to 1.5 billion parameters using TTFS coding. We also report an estimate of spike-related energy; this is a spike-count proxy under an established cost model rather than a measurement on neuromorphic hardware.

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Zhuoya Zhao, Parsa Omidi, Aref Jafari, Richard Naud. 2026-09-04. Large Language Models with At Most One Spike per Neuron. https://arxiv.org/abs/2609.05151

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