arXiv AI
SymbolicLight V1: Spike-Gated Dual-Path Language Modeling at High Encoder Spike Sparsity
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SymbolicLight V1: Spike-Gated Dual-Path Language Modeling at High Activation Sparsity
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 AI
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Spike-Aware C++ INT8 Inference for Sparse Spiking Language Models on Commodity CPUs
arXiv:2606. 03026v1 Announce Type: cross Abstract: Spiking language models expose activation sparsity that dense Transformer runtimes do not directly exploit.
arXiv Machine Learning
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Matterhorn: Masked Time-to-First-Spike Encoding by Reassigning the Silent State for Sparse and Energy-Efficient Spiking Transformers
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.
arXiv Machine Learning
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The Sparsity Ceiling: Where Spiking Networks Can and Cannot Trade Activity for Energy
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.
Hugging Face Trending Papers
Jul 29
The Sparsity Ceiling: Where Spiking Networks Can and Cannot Trade Activity for Energy
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 Computation and Language
Sep 10
SymbolicLight V2: Hybrid Neuromorphic Architecture and Sparse Execution for Low-Energy Language Inference
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...