Neuro-Symbolic Green Computing and Its Integration with UN Sustainability Goals
Bikram Pratim Bhuyan, Amar Ramdane-Chérif · Auerbach Publications eBooks · 2025
Neuro-symbolic green computing represents an innovative fusion of neuro-symbolic artificial intelligence (AI) and green computing, aiming to develop intelligent, sustainable, and energy-efficient computational systems. This interdisciplinary approach combines the robust learning capabilities of neural networks with the logical reasoning strengths of symbolic AI while adhering to environmentally sustainable practices throughout the lifecycle of computing resources. This chapter provides a comprehensive overview of neuro-symbolic AI and green computing, discussing their key components, benefits, and the challenges associated with their integration. By leveraging techniques such as neuro-vector-symbolic architectures, hybrid reasoning networks, bootstrapping language-image pre-training (BLIP) fine-tuning, and differentiable programming, neuro-symbolic green computing enhances energy efficiency, reduces carbon footprints, and prolongs hardware lifespan. The chapter also aligns these advancements with the United Nations Sustainable Development Goals, demonstrating their potential to promote clean energy, sustainable industrial practices, resilient infrastructure, responsible consumption, and climate action. Looking ahead, the future of neuro-symbolic green computing promises advancements in the integration of AI with renewable energy, the development of energy-efficient hardware, scalable AI systems, real-time environmental monitoring, AI-driven policy support, and continuous learning, ensuring that AI technologies contribute positively to global sustainability efforts.