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arXiv:2606. 03026v1 Announce Type: cross Abstract: Spiking language models expose activation sparsity that dense Transformer runtimes do not directly exploit.
arXiv:2606.03026v2 Announce Type: replace-cross Abstract: Binary spike activations allow a language-model runtime to read only active weight columns and replace multiplications by weight sums. We imp...
arXiv:2605. 21333v2 Announce Type: replace-cross Abstract: Natively trained spiking language models must preserve information across time while operating through sparse binary activations, a combination that has produced a persistent quality gap relative to dense Transformers.
arXiv:2607. 22785v1 Announce Type: cross Abstract: Apple-Silicon SoCs share CPU, GPU, and Neural Engine over one unified memory system, raising the question of whether transformer inference can be accelerated by splitting single operators across units.
arXiv:2606. 10493v1 Announce Type: cross Abstract: Local deployment of large Mixture-of-Experts (MoE) models falls short of the service quality achieved in cloud-scale environments, even under low-concurrency workloads.
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...