arXiv:2608. 19232v1 Announce Type: cross Abstract: Spiking point cloud networks usually scan space in a fixed, input-agnostic order, which leaves the most distinctive resource of spiking computation, the temporal evolution of the membrane potential, unused as a locus of decision-making.
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.
arXiv:2608. 07712v1 Announce Type: cross Abstract: A predictive model receives a self-supervised signal whenever the consequence of an action is observed.
arXiv:2604. 25688v2 Announce Type: replace Abstract: Binary spikes provide only two neuronal output states per timestep, limiting the response capacity of spiking neural networks (SNNs) under short simulation horizons.
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.
arXiv:2607. 27990v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) communicate through sparse binary spike events rather than dense activations, enabling energy-efficient inference on neuromorphic hardware and motivating their use in always-on, battery-powered edge systems.
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:2609.35910v1 Announce Type: new Abstract: Adaptive perception spends extra computation on inputs where perception is expected to improve. When perception feeds a downstream decision system, a b...
SpikeQ introduces a spike‑guided post‑training quantization framework for spiking neural networks, selecting weight levels by simulating downstream neurons to minimize spike errors and designing a reset‑aware grid for pre‑fire membrane potential quantization that avoids spike flips. The method outperforms existing grids on seven image‑processing benchmarks, maintaining full‑precision accuracy with 2‑bit pre‑fire potentials and staying within 2.59 points of full precision at 4 bits while drastically reducing memory and energy usage.
arXiv:2610. 33658v1 Announce Type: new Abstract: Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, yet their reliance on dense, continuous-valued computation presents a fundamental challenge for energy-constrained deployment.
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.
arXiv:2607. 02283v1 Announce Type: cross Abstract: In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state-space models, and MLPs.
SDPAD is a fully spike‑driven end‑to‑end planning pipeline for autonomous driving that converts a pre‑trained ANN perception stack into integer‑spike form, lifts multi‑view images into a bird’s‑eye‑view space with a spike‑driven depth distribution, and plans via a spiking query transformer that fuses ego, agent, and map queries. On the nuScenes benchmark it matches strong ANN planners with an average L₂ error of 0.40 m and a collision rate of 0.12 %, while consuming only 69.9 mJ—less than 2 % of recent ANN baselines. In closed‑loop NAVSIM evaluation it achieves 86.3 PDMS, surpassing the previous SNN planner SAD by 4.3 points and matching mainstream ANN planners at a fraction of their energy.