{"slug": "low-latency-activation-regularized-sparse-neural-operators-with-distillation", "title": "Low-Latency Activation-Regularized Sparse Neural Operators with Distillation Assistance Towards Real-Time Edge-Deployable Virtual Sensing", "summary": "Researchers propose the Sparse-Activation-ReLU (SAR) layer, a single-step alternative for neural operators that achieves over a fivefold improvement in the combined Latency-Error-Energy (LEE) metric compared with Variable Spiking Neuron (VSN) and Leaky Integrate-and-Fire (LIF) implementations on the Heat Exchanger dataset. The approach, which includes synthetic knowledge distillation, reduces LEE score by more than twofold and L2 error by more than twofold, and improves VSN with ReLU-based spiking loss and graph-neighbor thresholding, reducing L2 error by nearly sevenfold. This work aims to enable energy-efficient virtual sensing for edge deployment.", "body_md": "arXiv:2608.23987v1 Announce Type: new\nAbstract: Virtual sensing enables digital twins and safety-critical systems to reconstruct and forecast spatial-temporal physics in real time. However, conventional computational and data-driven methods often face challenges in generalization, latency, and energy efficiency for edge deployment. Neural operators offer a promising alternative but remain reliant on power-intensive hardware. Spiking neurons and neuromorphic computing can improve efficiency, yet surrogate-gradient training and multi-step spiking introduce convergence and latency challenges. We propose the Sparse-Activation-ReLU (SAR) layer, a single-step alternative that promotes activation sparsity without surrogate-gradient training while remaining compatible with event-based computing. Within a trunk-based NOMAD architecture, SAR achieves over a fivefold improvement in the combined Latency-Error-Energy (LEE) metric compared with Variable Spiking Neuron (VSN) and Leaky Integrate-and-Fire (LIF) implementations. We further analyze spiking entropy and feature usage and introduce synthetic knowledge distillation, reducing the LEE score by more than twofold. Finally, we improve VSN through a ReLU-based spiking loss and graph-neighbor thresholding. On the Heat Exchanger dataset, these approaches reduce L2 error by more than twofold and nearly sevenfold, respectively, while reducing spiking and spatial aggregation. Overall, the work presented is a step towards energy-efficient virtual sensing by providing an alternative framework that can be positioned towards neuromorphic or other edge device integration that can be a gold standard to compare latency, energy, and error performance for future efficient designs that are sparsity or brain-inspired spiking based.", "url": "https://wpnews.pro/news/low-latency-activation-regularized-sparse-neural-operators-with-distillation", "canonical_source": "https://www.machinebrief.com/news/low-latency-activation-regularized-sparse-neural-operators-w-hacs", "published_at": "2026-08-26 04:00:00+00:00", "updated_at": "2026-08-26 04:44:11.864124+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "neural-networks", "ai-research"], "entities": ["Sparse-Activation-ReLU (SAR)", "Variable Spiking Neuron (VSN)", "Leaky Integrate-and-Fire (LIF)", "NOMAD", "Heat Exchanger dataset"], "alternates": {"html": "https://wpnews.pro/news/low-latency-activation-regularized-sparse-neural-operators-with-distillation", "markdown": "https://wpnews.pro/news/low-latency-activation-regularized-sparse-neural-operators-with-distillation.md", "text": "https://wpnews.pro/news/low-latency-activation-regularized-sparse-neural-operators-with-distillation.txt", "jsonld": "https://wpnews.pro/news/low-latency-activation-regularized-sparse-neural-operators-with-distillation.jsonld"}}