🌿 Green AI: Designing Energy-Efficient AI Systems
International Workshop on Sustainable AI Development and Deployment
About This Course
Green AI: Designing Energy-Efficient AI Systems is an international workshop that focuses on reducing the environmental footprint of AI systems without compromising their capabilities. Participants will explore efficient algorithm design, model compression, hardware-aware AI, and sustainable cloud and edge deployment practices.
The workshop will feature frameworks and tools such as TensorFlow Lite, PyTorch Mobile, pruning and quantization techniques, low-rank approximation, energy profiling, carbon accounting platforms, and open-source libraries for model efficiency. Participants will also engage in discussions on regulatory compliance, carbon disclosures, and corporate sustainability strategies related to AI.
Aim
To equip AI engineers, researchers, and product developers with the knowledge and skills to design, develop, and deploy energy-efficient and sustainable AI systems, promoting responsible innovation that aligns with environmental goals and carbon reduction targets.
Workshop Objectives
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Raise awareness about the carbon footprint of AI systems
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Provide hands-on skills for building and deploying efficient AI models
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Foster cross-disciplinary knowledge bridging AI, hardware, and sustainability
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Encourage responsible innovation aligned with global climate goals
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Equip participants to become advocates of Green AI in their organizations
Workshop Structure
Day 1: Introduction to Green AI and the Carbon Footprint of AI
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What is Green AI?
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Defining Green AI and its importance in sustainable technology development.
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Carbon Footprint of AI
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Analyzing the energy consumption and carbon emissions associated with AI technologies, with a global context.
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Challenges and Opportunities
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Identifying the challenges and opportunities in reducing AI’s environmental impact across industries.
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Ethical and Policy Considerations
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Exploring the ethical considerations and policy frameworks supporting sustainable AI practices.
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Day 2: Efficient Algorithm Design & Edge AI for Low-Carbon Computing
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Efficient Algorithm Design
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Techniques for designing algorithms that minimize energy consumption without sacrificing performance.
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Edge AI and Low-Carbon Computing
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The role of Edge AI in reducing carbon emissions by decentralizing computation and enabling low-carbon solutions.
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Case Studies
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Real-world applications of energy-efficient AI across various industries.
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Designing for Sustainability
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Approaches to incorporating sustainability from the initial design phase through deployment.
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Day 3: Evaluation Tools & Dashboard Reporting on AI Sustainability Metrics
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Introduction to Evaluation Tools
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Overview of tools and methods for evaluating AI’s environmental impact and sustainability.
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Using CodeCarbon
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Practical guidance on integrating CodeCarbon to track and optimize AI’s carbon footprint.
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MLCO2 and Other Tools
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Utilizing MLCO2 and other platforms to assess and reduce the carbon emissions of AI systems.
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Dashboard Reporting
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Setting up dashboards to monitor and visualize AI sustainability metrics in real time.
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Continuous Monitoring
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Best practices for continuous monitoring and ongoing optimization of AI sustainability efforts.
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Who Should Enrol?
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AI engineers and machine learning practitioners
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Data scientists and software developers
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Sustainability managers and ESG officers
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Cloud architects and DevOps engineers
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Students (UG/PG/PhD) in AI, data science, or green computing
Important Dates
Registration Ends
06/23/2025
IST 4 PM
Workshop Dates
06/23/2025 – 06/25/2025
IST 5 PM
Workshop Outcomes
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Understand the environmental impact of AI model development and deployment
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Apply model compression and efficiency techniques to reduce carbon emissions
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Analyze and benchmark energy consumption across AI pipelines
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Integrate sustainable practices into AI workflows and corporate strategies
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Earn a certification in energy-efficient AI development and deployment
Meet Your Mentor(s)
Fee Structure
Student Fee
₹1999 | $50
Ph.D. Scholar / Researcher Fee
₹2999 | $60
Academician / Faculty Fee
₹3999 | $70
Industry Professional Fee
₹5999 | $90
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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