arXiv Machine Learning By Linhui Xiao, Guiping Cao, Mingyue Guo, Xianchao Guan, Fan Yang, Ming Tao, Xin Li, Yuxin Peng, Yaowei Wang

A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design

Read the original on arXiv Machine Learning →

arXiv:2607. 09084v1 Announce Type: new Abstract: The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
Jul 10

A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents

arXiv:2510. 22052v2 Announce Type: replace Abstract: The field of artificial intelligence (AI) has taken a tight hold on broad aspects of society, industry, business, and governance in ways that dictate the prosperity and might of the world's economies.

By Abhijit Chatterjee, Niraj K. Jha, Jonathan D. Cohen, Thomas L. Griffiths, Hongjing Lu, Diana Marculescu, Ashiqur Rasul, Wenrui Xu, Keshab K. Parhi
arXiv AI
Aug 10

Multi-Level Modeling of Large Language Model Inference Latency and Energy via Hybrid Analytical--Machine-Learning Predictors

arXiv:2608. 06723v1 Announce Type: cross Abstract: The rapid scaling of Large Language Models (LLMs) has significantly increased computational cost, energy consumption, and inference latency, making accurate estimation essential for sustainable artificial intelligence deployment and hardware-aware design.

By Saeid Shokoufa, Mohammad Erfan Sadeghi, Mehdi Kamal, Massoud Pedram
arXiv AI
Jul 28

OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence

arXiv:2607. 22805v1 Announce Type: cross Abstract: We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments.

By Keya Patel, Sajib Mistry, Sheik Mohammad Mostakim Fattah, Aneesh Krishna