Guest Editors
Dr. Jalal Sadoon Hameed Al-Bayati
Email: jalal.hameed@uobaghdad.edu.iq
Affiliation: Computer Engineering Camps, University of Baghdad, Baghdad, 10001, Iraq
Homepage:
Research Interests: core AI and machine learning, NLP, AI in engineering and design, AI-augmented optimization, sustainable AI
Dr. Mohammed Al-shammaa
Email: m.alshammaa@coeng.uobaghdad.edu.iq
Affiliation: Computer Engineering Camps, University of Baghdad, Baghdad, 10001, Iraq
Homepage:
Research Interests: core AI and machine learning, NLP, AI in engineering and design, AI-augmented optimization, sustainable AI
Summary
1) The rapid expansion of artificial intelligence has brought unprecedented computational demands, prompting urgent research into sustainable and energy-efficient AI systems. As global concerns over climate impact intensify, optimizing AI for minimal energy consumption has become a critical priority across both academia and industry.
2)This Special Issue aims to advance the field of sustainable and energy-efficient artificial intelligence by showcasing innovative research that reduces the environmental impact of AI systems. It invites contributions on model optimization techniques, low-power hardware architectures, edge computing strategies, and lifecycle assessments of AI deployments. The scope includes theoretical frameworks, applied methodologies, and interdisciplinary approaches that promote resource-aware AI development across domains such as natural language processing, computer vision, and engineering design. By fostering dialogue between academia and industry, the issue seeks to catalyze scalable solutions for greener AI technologies.
3) Special Issue Themes:
· Model Compression and Optimization Techniques
· Exploring pruning, quantization, distillation, and other methods to reduce computational load.
· Energy-Aware Neural Architectures
· Design and evaluation of novel architectures that balance performance with energy efficiency.
· Edge AI and Low-Power Deployment
· Strategies for deploying AI models on resource-constrained devices and embedded systems.
· Hardware Innovations for Green AI
· Advances in specialized processors, neuromorphic computing, and energy-efficient hardware.
· Lifecycle Assessment and Environmental Impact of AI Systems
· Quantifying and mitigating the carbon footprint across training, deployment, and maintenance phases.
Keywords
Sustainable AI, Green AI, Energy-efficient machine learning, Edge computing, Low-power AI systems, AI lifecycle assessment, Neuromorphic hardware, Responsible AI, Carbon footprint of AI, Efficient neural architectures, AI deployment optimization, Environmental impact of AI
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