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...
By Jingya Wang, Dehao Zhang, Shuai Wang, Malu Zhang, Yang Yang, Haizhou Li
arXiv:2609.39514v1 Announce Type: new
Abstract: Vision-language-action (VLA) models bridge multimodal understanding and robotic control, advancing the dominant paradigm for embodied intelligence. How...
By Shuai Wang, Malu Zhang, Mingquan Liu, Weihui Dai, Dehao Zhang, Jieyuan Zhang, Yimeng Shan, Zijian Zhou, Yang Yang
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.
By Aurora Micheli, Claudio Gallicchio, Nergis Tomen
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.
By Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara
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.
By Zhengzheng Tang
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.