rmems/SEMM-Latent-Telemetry

SAAQ-Latent-Telemetry

Bare-metal hardware telemetry and SNN latent space routing data for neuromorphic quantization research. This dataset documents the discovery of Semantic Attractor Clustering — that a Spiking Neural Network physically routes different semantic concepts (abstract language vs code syntax vs math logic) into distinct, repeatable biological pathways when L2 Normalization is applied to LLM embeddings.

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Dataset Details

• Curated by: Raul Montoya Cardenas
• Language(s): English, Code
• License: Apache 2.0 / MIT (dual-licensed)
• Repository: huggingface.co/datasets/rmems/SAAQ-Latent-Telemetry
• Base Model: allenai/OLMoE-1B-7B-0125-Instruct-GGUF
• Implementation: corinth-canal (SNN quantization pipeline)
• Analysis: Surrogate_Viz.jl (symbolic regression)

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Dataset Description

Origins of Metis

Before this was a formal dataset, it was an attempt to solve a bare-metal problem. I had been experimenting with mining telemetry, HFT bots, and sync node data to train a spiking neural network (SNN), but the data kept returning dead zeros in value after being used for training.

The breakthrough came entirely by accident. I was running heavy mods — DLSS 4.0 and path tracing — on Cyberpunk 2077 and the Resident Evil 4 Remake. My workstation PC was screaming, pushing harder and louder than it ever did during crypto mining. That sparked the realization: What if I used raw gaming telemetry data for neuromorphic spike data conversion? What if I could use this intense hardware stress to create an artificial heartbeat for AI?

When I pitched this idea, most people didn't believe the spike data conversion would work. But after refining the early thermal equations using that Resident Evil 4 telemetry, Metis was born — a MoE-based SNN quantization model and dataset architecture for exploring SNN quantization.

Relationship to Spikenaut

Spikenaut is my pure SNN model, built from scratch as a native spiking neural network. Metis (this repository) serves as the architect and teacher — exploring SNN quantization techniques through the OLMoE Mixture-of-Experts model. The discoveries, equations, and architecture frameworks developed here feed directly into Spikenaut's training and evolution. Metis proves the math; Spikenaut implements it natively.

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The Science: Semantic Attractor Clustering

This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the routing encoder. The objective is to map the physical routing of LLM embeddings (specifically from the allenai/OLMoE-1B-7B-0125-Instruct-GGUF Mixture of Experts model) as they are processed by biologically-inspired neuronal fatigue mechanics.

The Discovery: Physical Neighborhood Mapping

The primary breakthrough documented in this dataset is the organic, physical separation of semantic concepts into distinct routing bands. By applying L2 Normalization to the embeddings, the network bounds semantic pressure, forcing tokens to follow the biological path of least resistance.

Telemetry visualizations prove that the Spike-based routing physically routes different cognitive tasks into isolated biological neighborhoods:

Abstract Language Routing (2000-Route)	Structured Logic Routing (600-800 Band)
Abstract English logic establishes a dominant attractor basin at the 2000-index walker route	Rust syntax and math logic collapse into the same 600-800 frequency band

When fed abstract English logic, the network distributes energy across multiple nodes, establishing a dominant attractor basin at the 2000-index walker route, with secondary echoes in Walkers 700 and 1450.

When fed rigid mathematical statements or raw Rust syntax, the network completely abandons the 2000-route. The tokens experience mathematical pushback in abstract centers and organically collapse into the exact same 600-800 frequency band. This demonstrates that the network physically maps highly structured logic tasks to adjacent biological neighborhoods to conserve energy.

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Experiment Progression

The dataset documents the chronological progression from synthetic baselines to actual semantic routing:

Phase	Name	Input	Key Result
1	Synthetic Baseline	Synthetic sine wave	Verified GPU temporal loop (10,000 ticks) and basic biological fatigue
2	F16 Magnitude Collapse	Real LLM embeddings (OLMoE)	Unscaled F16-to-F32 extraction caused routing collapse (single walker overwhelmed)
3	Attractor Discovery	"Let's teach this MoE model..."	L2 Normalization shattered the collapse; energy settled into Walker 2000
4	Rust Syntax	fn main() { println!(); }	Code syntax routed to completely different biological neighborhood (600-800 band)
5	Math Logic Clustering	"The derivative of a constant is mathematically zero."	Math logic routed to the same 600-800 band as Rust syntax — confirming Semantic Attractor Clustering

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Usage

Quick Start (Python)

from datasets import load_dataset

# Load the hardware telemetry dataset
dataset = load_dataset("rmems/SAAQ-Latent-Telemetry", split="train")

print(dataset.features)
print(dataset[0])

Data Schema (Hardware Telemetry — Parquet)

The primary data is provided as Parquet files captured at 5ms intervals using NVML:

Feature	Type	Description
timestamp_ms	int64	UNIX timestamp in milliseconds (5ms interval)
power_usage_mw	uint32	Total GPU power usage in milliwatts
temperature_c	float32	GPU core temperature in Celsius
pcie_rx_kbps	uint32	Incoming PCIe throughput in KB/s (excitatory signal)
pcie_tx_kbps	uint32	Outgoing PCIe throughput in KB/s
encoder_util_perc	float32	NVIDIA Encoder (NVENC) utilization %
decoder_util_perc	float32	NVIDIA Decoder (NVDEC) utilization %
mangohud_active	bool	Whether MangoHud overlay was active
cpu_tctl_c	float32	CPU package temperature (Tctl)
cpu_ccd1_c	float32	CPU Core Complex Die 1 temperature
cpu_ccd2_c	float32	CPU Core Complex Die 2 temperature
throttle_reasons_bitmask	int64	Hardware throttling events bitmask (inhibitory signal)

Neuromorphic Mapping

This data behaves as "sensorimotor" stimulus for neural networks:

• Excitatory Inputs: High surges in pcie_rx_kbps indicate asset floods (e.g., BVH structure updates for path tracing), mimicking sensory signals
• Action Potentials: encoder_util_perc, decoder_util_perc, and power_usage_mw transients represent internal activity and firing rates
• Inhibitory Inputs: Non-zero throttle_reasons_bitmask signals act as inhibitory governors, dynamically suppressing activity
• State/Momentum: Slow-moving temperatures (cpu_tctl_c, temperature_c) and memory capacity

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Dataset Structure

├── origin_hardware_baselines/
│   └── resident_evil_4/
│       ├── system_telemetry_v1_batch_*.parquet   (48 files)
│       ├── RE4_path_tracing_telemetry.csv
│       └── README.md
├── first-day-testing-real-weights/
│   ├── first-test-falied/        # Routing collapse visualization
│   ├── second-test/              # English logic routing (2000-route)
│   ├── third-test/               # Rust syntax routing (600-800 band)
│   └── fourth-test/              # Math logic routing (600-800 band)
├── experiments/                  # Smoke test visualizations
├── SAAQ 1.0/                     # Foundational SAAQ equation
├── New symbolic regression equation/   # SAAQ 1.5 delta-Q adaptation
├── Research notes/               # Informal research notes
├── routing/                      # (placeholder — future routing CSVs)
├── results/
│   ├── plots/                    # (placeholder — visualizations)
│   └── raw_telemetry/            # (placeholder — tick logs)
└── model_metadata_manifest.json  # Structured project metadata

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Hardware Environment

Component	Spec
Workstation	Ship of Theseus
GPU	ASUS ProArt GeForce RTX 5080 (16GB VRAM)
CPU	AMD Ryzen 9 9950X
OS	Fedora 43
Implementation	Custom Rust/CUDA via corinth-canal

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Bias, Risks, and Limitations

• This dataset is generated from a single hardware configuration (RTX 5080 + Ryzen 9 9950X). Routing patterns may differ on other GPU architectures.
• The telemetry captures are from gaming workloads (Resident Evil 4 Remake with path tracing). Other GPU stress patterns may produce different "heartbeat" signatures.
• The SNN routing visualizations represent a specific quantization approach (SAAQ) applied to a specific model (OLMoE). Results may not generalize to other SNN architectures or MoE models.
• The dataset is small (48 parquet batches) and is intended for research and equation discovery, not large-scale training.

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Citation

BibTeX:

@dataset{montoya_2026,
  author = {Raul Montoya Cardenas},
  title = {SAAQ-Latent-Telemetry: Spikenaut SNN Routing},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/rmems/SAAQ-Latent-Telemetry}}
}

APA:

Montoya Cardenas, R. (2026). SAAQ-Latent-Telemetry: Spikenaut SNN Routing [Dataset]. Hugging Face. https://huggingface.co/datasets/rmems/SAAQ-Latent-Telemetry

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Glossary

Term	Definition
Walker	A pulse of electrical energy (spike) that physically explores the network to find the path of least resistance. Analogous to electrical impulses in a biological brain.
L2 Normalization	Bounds semantic pressure to the unit sphere, preventing any single neuron from becoming dominant. Mimics biological brain energy distribution.
Semantic Attractor Clustering	The phenomenon where an SNN physically maps different semantic concepts (abstract language vs code syntax) to distinct, repeatable biological pathways.
Fatigue Mechanics	Neurons that fire too much become less responsive, preventing energy overload and enabling network adaptation.
SAAQ	Semantic Attractor Architecture Quantization — the quantization technique developed through this research.

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License

This dataset is dual-licensed under Apache 2.0 and MIT.