🌿 Green AI: Designing Energy-Efficient AI Systems
International Workshop on Sustainable AI Development and Deployment
Skills you will gain:
About Program:
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.
Program 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
What you will learn?
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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Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
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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
Career Supporting Skills
Program 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