arXiv:2605.21333v3 Announce Type: replace-cross
Abstract: Natively trained spiking language models must preserve information across time while operating through sparse binary activations, a combinati...
By Ting Liu
arXiv:2606. 03026v1 Announce Type: cross Abstract: Spiking language models expose activation sparsity that dense Transformer runtimes do not directly exploit.
By Ting Liu
arXiv:2609.09772v1 Announce Type: new
Abstract: SymbolicLight V2 combines sparse event computation with continuous-state processing in a hybrid neuromorphic language architecture. Extending V1's spik...
By Ting Liu
Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates. We argue the energy dividend of sparsity is not a property of SNNs but of the task.
arXiv:2607. 26648v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates.
By Zeyu Wang
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.
By Zhanglu Yan, Kaiwen Tang, Zixuan Zhu, Zhenyu Bai, Qianhui Liu, Yongxin Zhu, Weng-Fai Wong