arXiv Machine Learning August 3, 2026 By Zhanglu Yan, Kaiwen Tang, Zixuan Zhu, Zhenyu Bai, Qianhui Liu, Yongxin Zhu, Weng-Fai Wong

Matterhorn: Masked Time-to-First-Spike Encoding by Reassigning the Silent State for Sparse and Energy-Efficient Spiking Transformers

Read the original on arXiv Machine Learning →

arXiv:2601. 22876v2 Announce Type: replace Abstract: Spiking neural networks (SNNs) promise energy-efficient inference for large language models (LLMs), yet most reported savings rely on compute-operation counts that overlook data movement.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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