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Spikenaut-SNN-Telemetry

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  1. README.md +123 -115
  2. data/parameters.mem +16 -0
  3. data/parameters_decay.mem +16 -0
  4. data/parameters_weights.mem +256 -0
  5. data/snn_model.json +1 -0
README.md CHANGED
@@ -2,137 +2,145 @@
2
  language:
3
  - en
4
  license: gpl-3.0
5
- multilinguality: monolingual
6
  size_categories:
7
- - n<1K
8
  task_categories:
9
  - time-series-forecasting
10
  task_ids:
11
- - univariate-time-series-forecasting
12
- pretty_name: Spikenaut SNN v2 - Fresh Blockchain Telemetry
13
- description: Fresh Kaspa and Monero blockchain telemetry data with Julia-Rust hybrid
14
- training results for Spikenaut v2 spiking neural network.
15
  tags:
16
- - blockchain
17
- - neural-networks
18
  - spiking-neural-networks
 
 
 
 
 
 
19
  - kaspa
20
  - monero
21
- - telemetry
22
- - hybrid-computing
23
- - julia-rust
24
- - e-prop
25
- - ottp
26
- dataset_info:
27
- features:
28
- - name: timestamp
29
- dtype: timestamp[ns]
30
- - name: blockchain
31
- dtype: string
32
- - name: event
33
- dtype: string
34
- - name: blocks_accepted
35
- dtype: float64
36
- - name: block_rate
37
- dtype: float64
38
- - name: telemetry
39
- struct:
40
- - name: gpu_temp_c
41
- dtype: float64
42
- - name: hashrate_mh
43
- dtype: float64
44
- - name: power_w
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- dtype: float64
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- - name: qubic_epoch_progress
47
- dtype: float64
48
- - name: qubic_tick_trace
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- dtype: float64
50
- - name: reward_hint
51
- dtype: float64
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- - name: timestamp_unix
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- dtype: float64
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- - name: hour_of_day
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- dtype: int64
56
- - name: day_of_week
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- dtype: int64
58
- - name: hashrate_normalized
59
- dtype: float64
60
- - name: power_efficiency
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- dtype: float64
62
- - name: thermal_efficiency
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- dtype: float64
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- - name: spike_hashrate
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- dtype: int64
66
- - name: spike_power
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- dtype: int64
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- - name: spike_temp
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- dtype: int64
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- - name: spike_qubic
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- dtype: int64
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- - name: composite_reward
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- dtype: float64
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- - name: target_hashrate_change
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- dtype: float64
76
- - name: target_power_change
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- dtype: float64
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- - name: current_height
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- dtype: float64
80
- - name: total_height
81
- dtype: float64
82
- - name: sync_percent
83
- dtype: float64
84
- - name: remaining_blocks
85
- dtype: float64
86
- splits:
87
- - name: train
88
- num_bytes: 1189
89
- num_examples: 5
90
- - name: validation
91
- num_bytes: 237
92
- num_examples: 1
93
- - name: test
94
- num_bytes: 472
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- num_examples: 2
96
- download_size: 39796
97
- dataset_size: 1898
98
- configs:
99
- - config_name: default
100
- data_files:
101
- - split: train
102
- path: data/train-*
103
- - split: validation
104
- path: data/validation-*
105
- - split: test
106
- path: data/test-*
107
  ---
108
 
109
- # 🦁 Spikenaut-SNN-v2 - Complete Neuromorphic Blockchain Ecosystem
110
 
111
- **The world's most comprehensive open neuromorphic dataset** — 635 MB of production-ready data across 5 complete collections.
 
 
 
112
 
113
- **Live March 2026 telemetry + your real trained parameters + massive legacy data**
114
 
115
- ### 📊 What's Inside (v2.1)
116
 
117
- | Collection | Size | Records | Content |
118
- |-------------------------|----------|-------------|--------|
119
- | Core Telemetry | 200 MB | Enhanced samples | Live Kaspa (8–13 blocks/sec), Monero, Qubic + spike encodings |
120
- | Training Data | 43 KB | ~40K+ | Real SNN spike patterns with reward signals |
121
- | Mining Operations | 55 MB | Millions | Full BzMiner v24.0.1 logs (hashrate, GPU temp, power) |
122
- | System Operations | 1 KB | Events | Supervisor telemetry & lifecycle monitoring |
123
- | Research Dataset | 380 MB | ~400K+ | Advanced neuromorphic records |
 
 
 
 
 
124
 
125
- **Your actual trained weights** (16×16 architecture, 95.2% accuracy, 35 µs/tick) are included in multiple formats:
126
- - Q8.8 `.mem` files (FPGA-ready)
127
- - PyTorch `.pth` + `.safetensors`
128
- - Analysis JSON
 
129
 
130
- ### 🚀 Quick Start
 
 
131
 
132
  ```python
133
- from datasets import load_dataset
134
- ds = load_dataset("rmems/Spikenaut-SNN-v2-Telemetry-Data-Weights-Parameters")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
135
 
136
- # Load your real trained parameters
137
- import torch
138
- params = torch.load("your_real_parameters/spikenaut_your_weights.pth")
 
2
  language:
3
  - en
4
  license: gpl-3.0
 
5
  size_categories:
6
+ - 1M<n<10M
7
  task_categories:
8
  - time-series-forecasting
9
  task_ids:
10
+ - multivariate-time-series-forecasting
11
+ pretty_name: Spikenaut SNN v2 — GPU Telemetry & Market Data
 
 
12
  tags:
 
 
13
  - spiking-neural-networks
14
+ - neuromorphic
15
+ - gpu-telemetry
16
+ - cryptocurrency
17
+ - blockchain
18
+ - dynex
19
+ - qubic
20
  - kaspa
21
  - monero
22
+ - liquid-state-machine
23
+ - stdp
24
+ - julia
25
+ - rust
26
+ - cuda
27
+ ---
28
+
29
+ # Spikenaut SNN v2 — GPU Telemetry & Market Data
30
+
31
+ **Author:** Raul Montoya Cardenas
32
+ **Institution:** Texas State University — Micro & Nano Device Systems (Spring 2026)
33
+ **Project:** [Eagle-Lander / Ship of Theseus](https://github.com/rmems/ship_of_theseus_rs)
34
+
35
+ Real hardware telemetry and live market data collected from an RTX 5080 mining rig running
36
+ DNX, Qubic, Kaspa, and Monero workloads. Used to train and validate the Spikenaut V2
37
+ 4-lobe Ensemble Liquid State Machine (262,144 LIF neurons, CUDA-accelerated).
38
+
39
+ ---
40
+
41
+ ## Dataset Contents
42
+
43
+ | File | Rows | Size | Description |
44
+ |------|------|------|-------------|
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+ | `data/neuromorphic_data.jsonl` | 789,114 | 363 MB | Primary GPU telemetry — voltage, temp, hashrate, power, clock, Qubic tick traces |
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+ | `data/ghost_market_log.jsonl` | 223,020 | 174 MB | Live market ticks — DNX, Qubic, Kaspa, Monero, Ocean, Verus price & volatility |
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+ | `data/node_sync_harvest.jsonl` | 120,334 | 55 MB | Blockchain node sync events — Kaspa, Monero, Dynex block acceptance rates |
48
+ | `data/snn_model.json` | — | 3.8 KB | Trained SNN model weights snapshot |
49
+ | `data/parameters.mem` | — | 80 B | FPGA-compatible membrane potential parameters (Q8.8 fixed-point) |
50
+ | `data/parameters_decay.mem` | — | 80 B | FPGA-compatible decay parameters |
51
+ | `data/parameters_weights.mem` | — | 1.3 KB | FPGA-compatible synaptic weight parameters |
52
+
53
+ **Total: ~1.13M rows, 592 MB**
54
+
55
+ ---
56
+
57
+ ## Telemetry Schema (`neuromorphic_data.jsonl`)
58
+
59
+ Each row is a JSON object with a `telemetry` key:
60
+
61
+ ```json
62
+ {
63
+ "telemetry": {
64
+ "vddcr_gfx_v": 0.7115,
65
+ "vram_temp_c": 59.0,
66
+ "gpu_temp_c": 51.0,
67
+ "hashrate_mh": 0.0,
68
+ "power_w": 156.9,
69
+ "gpu_clock_mhz": 2872.0,
70
+ "mem_clock_mhz": 14801.0,
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+ "fan_speed_pct": 30.0,
72
+ "mem_util_pct": 27.0,
73
+ "power_z_score": 0.0,
74
+ "temp_z_score": 0.0,
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+ "clock_z_score": 0.0,
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+ "qubic_tick_trace": 0.935,
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+ "qubic_tick_rate": 1.0,
78
+ "qubic_epoch_progress": 1.0,
79
+ "ocean_intel": 0.0,
80
+ "qu_price_usd": 0.0
81
+ }
82
+ }
83
+ ```
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+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
85
  ---
86
 
87
+ ## Hardware
88
 
89
+ - **GPU:** NVIDIA RTX 5080 (16 GB VRAM, Blackwell sm_120)
90
+ - **Mining workloads:** DNX (Dynex), Qubic, Kaspa (KHeavyHash), Monero (RandomX)
91
+ - **FPGA:** Artix-7 Basys3 — SNN reflex arc co-processor
92
+ - **OS:** RHEL 8 / Fedora
93
 
94
+ ---
95
 
96
+ ## SNN Architecture This Data Trains
97
 
98
+ ```
99
+ MarketPulse (14 channels) + GPU Telemetry
100
+ ↓
101
+ [4 Parallel LIF Lobes]
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+ ├── Scalper τ=10ms w=0.4
103
+ ├── Day τ=25ms w=0.3
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+ ├── Swing τ=50ms w=0.2
105
+ └── Macro τ=100ms w=0.1
106
+ ↓
107
+ Weighted Readout (16 channels)
108
+ Bull/Bear signals + confidence
109
+ ```
110
 
111
+ - 262,144 total neurons (4 × 65,536)
112
+ - Sparse recurrent connectivity (1%, ~42M synapses)
113
+ - Ornstein-Uhlenbeck stochastic dynamics
114
+ - STDP covariance learning
115
+ - Hardware proprioception: GPU temp/power → global inhibition
116
 
117
+ ---
118
+
119
+ ## Usage
120
 
121
  ```python
122
+ import json
123
+
124
+ # Load GPU telemetry
125
+ with open("data/neuromorphic_data.jsonl") as f:
126
+ for line in f:
127
+ row = json.loads(line)
128
+ t = row["telemetry"]
129
+ print(t["gpu_temp_c"], t["power_w"], t["qubic_tick_rate"])
130
+
131
+ # Load market ticks
132
+ with open("data/ghost_market_log.jsonl") as f:
133
+ for line in f:
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+ tick = json.loads(line)
135
+ print(tick)
136
+ ```
137
+
138
+ ---
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+
140
+ ## License
141
+
142
+ GPL-3.0 — same as the [Eagle-Lander](https://github.com/rmems/ship_of_theseus_rs) project.
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+
144
+ ---
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146
+ *Collected on ShipOfTheseus — Texas. All telemetry sanitized via the internal privacy scrubber.*
 
 
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data/snn_model.json ADDED
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