lori-framework-site

LORI-AE: Photonic AI Energy Sustainability Module

The Photonic AI Energy Sustainability Module (LORI-AE) is an extension of the Lori Framework designed to assess the energy and environmental impact of large-scale AI deployment.

As AI systems expand into physical infrastructure—robots, data centers, edge devices—the demand for power grows exponentially. This module aims to ensure that AI deployment remains sustainable, ethical, and aligned with planetary resource limits.

Core Submodules

1. GECA – GPU Energy Consumption Analyzer
2. REDE – Robot Energy Deployment Estimator
3. DCHFM – Data Center Heat Footprint Mapper
4. SEM – Sustainable Energy Matchmaker
5. AEWS – AI-Overuse Early Warning System

Key Metrics

• Energy per TFLOP (kWh/TFLOP)
• Carbon Footprint per Inference Task (gCO₂e/inference)
• Power Usage Effectiveness (PUE)
• Robotic Daily Energy Demand Curve
• Sustainable Deployment Index (SDI)

Submodule Descriptions

Submodule	Description
GECA	Analyzes energy usage per AI model, session, and architecture
REDE	Simulates robotic operation energy profiles, including movement, idle, and sensing
DCHFM	Maps power usage and cooling requirements across global climates
SEM	Recommends deployment zones based on regional energy profiles
AEWS	Triggers alerts when AI deployment exceeds sustainable thresholds

Applications

• Policy design for AI energy regulation
• AI infrastructure planning for corporations
• Climate-aware AI research deployment
• Ethical AI diplomacy and sustainability treaties

---

This module reflects the principle that intelligence must illuminate, not consume. AI must serve both progress and the planet.

Part of the Lori Framework

This site is open source. Improve this page.