💤 Sleeping GPU Inference Network (SGIN)
**Energy-aware AI inference that knows when *not* to run.**
SGIN is a local-first inference framework that decides **CPU / GPU / SKIP**
based on time, hardware state, and energy impact — helping reduce unnecessary
compute, cost, and carbon emissions.
## 🌍 Why SGIN?
Most AI systems assume:
*Inference must always run.*
SGIN asks a better question:
**Should inference run right now?**
By respecting **time windows**, **hardware conditions**, and **energy impact**,
SGIN avoids waste and promotes sustainable AI execution.
## ✨ Key Capabilities
- 🧠 **Policy-driven decisions**: CPU, GPU, or SKIP
- 🌙 **Time-aware scheduling** (work-life & power policies)
- 🔋 **Energy estimation** (mWh)
- 🌱 **Carbon estimation** (gCO₂)
- 📊 **CSV telemetry & daily summaries**
- 📈 **Auto-generated plots**
- ☁️ **Cloud vs local carbon comparison**
## 🧪 Quick Start
Run inference with decision logic:
python -m sleeping\_gpu\_inference.demo.run\_inference
Run daily summary:
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python -m sleeping\_gpu\_inference.demo.run\_daily\_summary
Generate plots:
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python -m sleeping\_gpu\_inference.demo.run\_plots
📁 Project Structure
bash
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sleeping\_gpu\_inference/
├── demo/ # Runnable entry points
├── runtime/ # CPU / GPU execution engines
├── profiler/ # Hardware state detection
├── scheduler/ # Time-based policies
├── energy/ # Energy \& carbon estimation
├── logging/ # CSV \& decision logs
├── reports/ # Summaries \& plots
├── plots/ # Generated PNG graphs
└── README.md
🧠 How Decisions Work
SGIN evaluates, in order:
⏰ Time policy (allowed window?)
🔌 Power state (plugged in?)
🎮 GPU state (idle \& cool?)
🖥️ CPU load
❌ Otherwise → SKIP
Skipping inference is treated as a valid, optimal decision.
📊 Metrics \& Visuals
SGIN generates plots directly from real execution data.
🔋 Energy Usage Over Time
🌍 Carbon Emissions Over Time
🧠 Decision Distribution
☁️ Cloud vs SGIN (Why Local Wins)
For the same workload:
Cloud inference uses always-on datacenter GPUs
SGIN runs only when needed
SKIP = zero energy, zero carbon
Daily reports quantify carbon saved by avoiding cloud inference.
🌱 Design Philosophy
Prefer not running over running inefficiently
Use estimates, not fake precision
Optimize for policy \& behavior, not benchmarks
Make sustainability measurable
🛣️ Roadmap
YAML-based policy configuration
Weekly / monthly reports
Cloud vs SGIN overlay graphs
GPU acceleration path
Blog / whitepaper version
🤝 Contributing
Ideas, issues, and discussions are welcome.
This project is about better decisions, not bigger models.
📄 License
MIT License
👤 Author
Santhanu
GitHub: https://github.com/santhanuss
Building thoughtful, energy-aware AI systems.