The paper introduces the Spiking Ternary GRU (stGRU), a bio‑inspired architecture that couples ternary‑weight gated recurrent units with resonate‑and‑fire neurons to enable efficient, matrix‑MAC‑free spiking neural networks for speech recognition. Trained end‑to‑end on LibriSpeech, the stGRU achieves single‑digit word error rates (6.44% on test‑clean) without an external language model, and also attains competitive character error rates on Aishell‑1. The model develops a depth‑graded hierarchy where shallow layers filter acoustic signals and deeper layers integrate semantic context, mirroring hierarchical processing in human cortex.
arXiv:2606. 19039v1 Announce Type: cross Abstract: The mismatch between continuous acoustic signals and discrete event-driven processing remains a fundamental bottleneck for neuromorphic speech processing.
arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.
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.
The paper presents a spiking neural network (SNN) approach that uses time-to-first-spike (TTFS) coding to limit each neuron to at most one spike per time window, enabling energy-efficient large language models (LLMs). A reference-based strategy is introduced to encode the four core LLM components—embedding layers, layer normalization, attention-related operations, and dropout—allowing the construction of a fully TTFS-based SNN architecture trained end-to-end. Experiments on BERT and GPT-2 show performance comparable to artificial neural network (ANN) counterparts on natural language understanding and common-sense reasoning, while achieving a 1.5‑billion‑parameter spiking LLM and providing an estimate of spike-related energy consumption.
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.
NeuronSpark is a self‑attention‑free language model that uses input‑dependent spiking membrane dynamics, allowing each neuron’s decay rate, gain, and firing threshold to adapt to the current token. This selective spiking approach enables billion‑parameter pretraining from random initialization without relying on backbone transfer, conversion, or distillation, achieving competitive zero‑shot performance and a fast–slow hierarchy. On a 1.1B‑parameter, 2.1B‑token training run, NeuronSpark attains a 40.0 eight‑task zero‑shot average, surpasses comparable‑budget Pythia/Mamba checkpoints, and demonstrates significant energy and cycle reductions on FPGA implementations.
The paper introduces HRF-Res, a spiking reservoir computing model that replaces traditional Leaky Integrate-and-Fire neurons with a heterogeneous mix of Harmonic Resonate-and-Fire neurons. These HRF neurons possess intrinsic oscillatory dynamics that selectively respond to specific input frequencies, allowing the reservoir to capture frequency‑specific temporal patterns. As a result, HRF-Res delivers more discriminative representations and higher classification accuracy across ten diverse benchmarks while maintaining the energy efficiency of spiking computation, achieving up to 19× lower energy consumption than comparable non‑spiking oscillatory reservoirs.
arXiv:2610.09710v1 Announce Type: new Abstract: ANN-to-SNN conversion offers a practical route toward energy-efficient spiking Vision-Language-Action (VLA) models by bypassing the substantial cost of...
arXiv:2606. 13016v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are promising for energy-efficient inference, and time-to-first-spike (TTFS) coding is especially attractive because each neuron fires at most once.
arXiv:2609.13899v1 Announce Type: cross Abstract: The BabyLM challenge measures how much language a model can learn from developmentally-plausible, child-scale data rather than internet-scale corpora...
arXiv:2608.30792v1 Announce Type: cross Abstract: Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificia...
The paper introduces the Dormant Spiking Neuron (DSN), a state-driven mechanism that allows neurons in spiking neural networks to enter a dormant state when their membrane potential falls below a negative threshold. This dynamic gating prunes redundant computations at the instance level and is trained end‑to‑end via backpropagation through time with surrogate gradients. Experiments on static and neuromorphic datasets, including ImageNet, show that DSN reduces computational energy by 40% to over 70% and improves baseline SNN accuracy by 1.34%, outperforming state‑of‑the‑art static pruning methods.