I made a CPU only spiking neuron network lib that comes pretty close to PyTorch
A new library called NeuronGuard has been developed for spiking neural networks, showing promising results compared to PyTorch. It achieved 93.14% accuracy while training on 1,000,000 articles from the Wikimedia dataset in just 15.85 seconds on a standard Apple Silicon CPU. NeuronGuard's architecture allows for efficient training without the need for GPU acceleration, making it suitable for edge devices.
- ▪NeuronGuard is a neuromorphic spiking neural network library that operates efficiently on CPU hardware.
- ▪The model was trained on 1,000,000 articles and achieved 93.14% accuracy in a short training time.
- ▪It is significantly faster than PyTorch, with a training time of 15.85 seconds compared to 163.84 seconds for PyTorch with 10 epochs.
2 outlets in our directory ran this story, first to last over 1 hour. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Event like spiking neuron lib that fits into the CPU cache [P] — r/MachineLearning
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Huggingface |
| Canonical URL | https://huggingface.co/etoxin/neuronguard-wikipedia-classifier |
| Publication time | Sat, 30 May 2026 04:00:35 +0000 |
| Retrieval time | 2026-05-30T04:11:57.314Z |
| Last seen | 2026-05-30T04:11:57.314Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | PU6tELUKjrQr · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
etoxin / neuronguard-wikipedia-classifier like 0 Rust wikimedia/structured-wikipedia English neuromorphic spiking-neural-networks edge-ai green-ai License: apache-2.0 Model card Files Files and versions xet Community Copy to bucket new Neuromorphic Wikipedia Domain Classifier Performance Metrics (Standard Apple Silicon CPU) Comparison: NeuronGuard vs. PyTorch (1,000,000 Samples) Head-to-Head Results (Apple Silicon M-Series CPU/GPU)Architectural Trade-OffsTechnology Overview How to Load and Use in Python Open Source & Community Neuromorphic Wikipedia Domain Classifier This repository hosts the pre-trained vocabulary and synaptic weights for NeuronGuard, a proof of concept that uses cache-aligned Spiking Neural Network (SNN) and neuromorphic event engine written in Rust and exposed to…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Huggingface.
Discussion
0 commentsMore from Huggingface
