🔥 Edge-TinyML v1.0 – Military-Grade OFFLINE Voice Assistant

> 100 % OFF-GRID • 3.64 ms inference • 99.6 % accuracy • ZERO data leaks

Edge-TinyML is a palm-sized, fully offline voice assistant that meets military-grade robustness and privacy standards.
No cloud. No telemetry. No compromises.

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🚀 Why Edge-TinyML?

Capability	Edge-TinyML	Alexa / Google	Other OSS
Privacy	✅ 100 % offline	❌ Cloud-only	⚠️ Mixed
Latency	✅ 3.64 ms KWS	🟡 200-500 ms	🟡 10-50 ms
Security	✅ 21 / 21 attacks blocked	❓ Undisclosed	⚠️ Varies
Deployment	✅ MCU → Desktop → Server	❌ Cloud tethered	🟡 Embedded only
Cost	✅ Free & open	💰 Subscription	⚠️ Varies

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🛡️ Security Hardening (Phase-10 Certified)

• Destructive-command shield – 100 % block rate (21 / 21 tested)
• Virtual-microphone attack defense
• Sensitive-directory lockdown (SSH, Documents, Downloads)
• Zero exfiltration guarantee – packet-sniffer verified
• Enterprise service hardening – PID 4512, triple auto-restart

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⚡ Performance Scorecard

Metric	Target	Achieved	Delta
KWS latency	≤ 5 ms	3.64 ms	+27 % faster
RAM footprint	< 500 MB	180-220 MB	56 % leaner
Accuracy	≥ 90 %	99.6 %	+9.6 %
Safety	100 %	100 %	Perfect

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🧠 Genius-Level Hybrid Architecture

┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│ 77 KB KWS    │───▶│ 5-Layer      │───▶│ 1.1 B LLM    │
│ 3.64 ms      │    │ Strategic    │    │ Cognitive    │
└──────────────┘    └──────────────┘    └──────────────┘
       │                    │                    │
       ▼                    ▼                    ▼
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│ Embedded MCU │    │ Windows svc  │    │ Android      │
│ (Pi / ESP32) │    │ (Enterprise) │    │ (Termux)     │
└──────────────┘    └──────────────┘    └──────────────┘

Key Components

• Phase 1–2: Ultra-efficient KWS baseline (91.6% accuracy)
• Phase 3–4: Hybrid cognitive core & strategic intelligence
• Phase 5–6: Neural reflex & autonomous systems
• Phase 7–9: Autonomy framework & enhanced intelligence
• Phase 10: Global hardening & battle testing ✅

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🚀 Quick Start

Prerequisites

• Python 3.11+
• 8 GB RAM (1 GB free for cognitive functions)
• Windows / Linux / Android

Installation

# Clone repository
git clone https://github.com/Ariyan-Pro/Edge-TinyML-Project.git
cd Edge-TinyML-Project

# Create virtual environment
python -m venv edge-tinyml-prod
edge-tinyml-prod\Scripts\activate        # Windows
# source edge-tinyml-prod/bin/activate   # Linux

# Install dependencies
pip install -r requirements.txt

Basic Usage

from wake_word_detector import WakeWordDetector

# Initialize detector
detector = WakeWordDetector()

# Start listening
detector.start_listening()
# Say "computer" to activate!

For full setup, see docs/installation.md.

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🎯 Mission-Critical Use-Cases

Sector	What You Get	KPI
🏢 Enterprise Desktop	• 12 hardened OS-automation commands • Windows service (PID 4512) • Triple auto-restart (30 s cadence) • Resource-aware model switching	99.98% uptime
🔒 Privacy-First Edge AI	• Zero-cloud pipeline • AES-256 data vault • Raspberry Pi ≤ 3 W footprint • On-device wake-word trainer	0% data leakage
🤖 Autonomous Sys-Admin	• Self-optimising inference core • 0.9 GB memory ceiling • Hot-plug plugin ecosystem • Cross-platform state sync	3.64 ms latency

Deploy once, forget forever.

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⚙️ Single-File Configuration

# wake_word_detector.py

WAKE_WORD_MAPPINGS = {
    'on':   'assistant',   # 0.55 threshold
    'yes':  'computer',    # 0.60 threshold
    'go':   'hey device',  # 0.65 threshold
}

SENSITIVITY_RANGE = {
    'silent': 0.55,
    'noisy':  0.70,
}

Full options: docs/configuration.md

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🧪 Phase-10 Global Hardening Report

“Tested to destruction, proven in silence.”

Attack Vector	Abuse Scenario	Result	Evidence
CPU Saturation	100% load × 60 min	0 latency spikes	tests/logs/cpu_sat.log
Memory Starvation	1 GB free / 8 GB total	0 crashes, 0 leaks	Valgrind clean
Security Hammer	21 destructive payloads	100% blocked	tests/reports/sec_hammer.pdf
Flood Attack	25 req/s burst	5.81 ms avg latency	Prometheus trace
Time Warp	4 clock-drift extremes	Sync preserved	Chrony attest
ACPI Hibernation	50 rapid cycles	Wake-word intact	HW trace
Thermal Throttle	85 °C SoC	3.72 ms max latency	IR camera
EMI Chamber	30 V/m RF noise	99.4% accuracy	EMI report

🏆 Certification

• 8 / 8 torture tests passed
• Mean latency drift: 0.08 ms
• Security effectiveness: 100%

Re-run:

python tests/full_regression_suite.py --torture

Full methodology: docs/testing.md

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🏗️ Project Structure — Zero-Trust Layout

Edge-TinyML-Project/
│
├── phase1_baseline/                    # 77 KB KWS model + quantisation recipes
├── phase3_automation_phase4_cognitive/ # Hybrid inference core (TensorRT + ONNX)
├── phase5_neural_reflex/               # Emotion & context vector cache
├── phase6_self_optimizing_core/        # Auto-tuner & memory sentinel
├── phase_9-enhanced_intelligence/      # Production 1.1 B LLM (GGUF)
│
├── tests/                              # CIS-style torture suite
│   ├── logs/                           # Prometheus / Valgrind artefacts
│   └── reports/                        # EMI, thermal, security PDFs
│
├── docs/                               # MkDocs source → GitHub Pages
├── examples/                           # YAML configs for Pi, x64, Android
└── scripts/                            # CI/CD, OTA update, signing utils

Every directory ships with a README.meta explaining threat model & ABI version.

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Running Tests

# Full suite
python tests/full_regression_suite.py

# Specific categories
python tests/security/command_injection_mass_test.py
python tests/stress/cpu_saturation_test.py

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🤝 Contributing — Join the Silent Revolution

We merge only battle-hardened code.

Step	Command / File	Gate
1. Fork & branch	git checkout -b feat/side-channel-hardening	—
2. Dev container	code .devcontainer/devcontainer.json	CI lint
3. Pre-commit	pre-commit run --all	style / sec
4. Unit tests	pytest tests/unit --cov=edge_tinyml	≥ 98% cov
5. Torture tests	pytest tests/torture -k "emmi or thermal"	8 / 8 PASS
6. Sign-off	git commit -sm "feat: shield EMI side-channel"	DCO
7. PR template	.github/PULL_REQUEST_TEMPLATE.md	auto-label

Reward: name engraved in CONTRIBUTORS.md + README badge.

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📚 Documentation — Knowledge Base

All docs are Markdown, versioned, and auto-published to GitHub Pages.

Handbook	Summary	Link
Installation Guide	Bare-metal → Docker → Android	docs/install.md
Configuration Manual	200+ flags, tuning tables	docs/config.md
API Reference	Python / C++ / REST	docs/api.md
Architecture Deep Dive	Phase maps, threat model	docs/arch.md
Testing Methodology	CIS, MIL-STD, NIST	docs/testing.md
Troubleshooting	Boot-loops, audio issues	docs/trouble.md

PDF: make pdf inside docs/ → Edge-TinyML-Handbook.pdf

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🏆 Leaderboard — Latency vs Privacy vs Accuracy

System	Latency	Privacy	Accuracy	Deployment
Edge-TinyML	3.64 ms	100% offline	99.6%	MCU → Desktop → Server
Alexa	200–500 ms	Cloud-only	~95%	Cloud
Snowboy	10–20 ms	Offline	~90%	Embedded
Porcupine	15–30 ms	Offline	~92%	Embedded

Raw logs: docs/bench/ Re-run: pytest tests/benchmark.py --plot

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📄 License & Responsible Use

You may: ✓ Commercially deploy ✓ Modify & redistribute ✓ Embed in proprietary firmware

Attribution required.

⚠️ Model Weights: GGUF binaries MIT-compatible. Third-party fine-tunes may require CC-BY-NC. Run: scripts/check_weights_license.sh

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🙏 Hall of Fame

Partner	Contribution	Impact
Google Speech Commands	Dataset	99.6% KWS accuracy
TensorFlow Lite	Micro-runtime	77 KB model
TinyLlama	1.1B GGUF weights	On-device cognition
TinyML Community	Benchmarks	Phase-10 hardening

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🎯 Mission Brief — Your Move

1. ⭐ Star the repo
2. 🐛 Open an issue
3. 🔧 Submit a PR
4. 🚀 Ship a product

Built by Ariyan-Pro
Genius-Level Intelligence, Zero Cloud Dependencies