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   https://doi.org/10.1145/3727200.3727207  

   Liu, Yejia; Li, Pengfei; Wong, Daniel; Ren, Shaolei (December 2024, ACM SIGEnergy Energy Informatics Review)

   The enormous growth of AI computing has led to a surging demand for electricity. To stem the resulting energy cost and environmental impact, this paper explores opportunities enabled by the increasing hardware heterogeneity and introduces the concept of Geographical Server Relocation (GSR). Specifically, GSRphysicallybalances the available AI servers across geographically distributed data centers subject to AI computing demand and power capacity constraints in each location. The key idea of GSR is to relocate older and less energy-efficient servers to regions with more renewables, better water efficiencies and/or lower electricity prices. Our case study demonstrates that, even with modest flexibility of relocation, GSR can substantially reduce the total operational environmental footprints and operation costs of AI computing. We conclude this paper by discussing major challenges of GSR, including service migration, software management, and algorithms. 

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2. Brain-Inspired Energy Efficient Technologies for Next-Generation Artificial Intelligence

   Chiel, Hillel; Coggan, J; Datta, G; Fellous, JM; Nourse, W; Quinn, R; Thomas, P (February 2026, Biological cybernetics)

   Since the advent of widely accessible AI tools, AI technology has been in high demand by businesses, academic researchers and individuals. Technology companies are building AI infrastructure at a rapid pace, and these facilities consume vast and growing resources, particularly electricity and water, with significant real and projected climate impacts. There is a need for new research initiatives to support long time horizon efforts to develop energy efficient computing capabilities to support the continued growth of AI infrastructure in a sustainable fashion. Such efficiency is required at both the hardware and software levels. Where can industry turn for examples of ultra-low power, energy efficient computing? We argue here that neurobiological principles offer rich and under-exploited sources of inspiration for energy efficient NeuroAI, and that new partnerships between industry and academia should be developed in this direction 

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3. Brain-inspired energy efficient technologies for next-generation artificial intelligence

   Chiel, Hillel J; Coggan, Jay A; Datta, Gourav; Fellous, Jean-Marc; Nourse, William; Quinn, Roger D; Thomas, Peter J (February 2026, Biological cybernetics)

   Since the advent of widely accessible AI tools, AI technology has been in high demand by businesses, academic researchers and individuals. Technology companies are building AI infrastructure at a rapid pace, and these facilities consume vast and growing resources, particularly electricity and water, with significant real and projected climate impacts. There is a need for new research initiatives to support long time horizon efforts to develop energy efficient computing capabilities to support the continued growth of AI infrastructure in a sustainable fashion. Such efficiency is required at both the hardware and software levels. Where can industry turn for examples of ultra-low power, energy efficient computing? We argue here that neurobiological principles offer rich and under-exploited sources of inspiration for energy efficient NeuroAI, and that new partnerships between industry and academia should be developed in this direction 

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4. Memory-Based Computing for Energy-Efficient AI: Grand Challenges

   https://doi.org/10.1109/VLSI-SoC57769.2023.10321880  

   Karimzadeh, Foroozan; Imani, Mohsen; Asgari, Bahar; Cao, Ningyuan; Lin, Yingyan; Fang, Yan (October 2023, IEEE)

   The remarkable progress in artificial intelligence (AI) has ushered in a new era characterized by models with billions of parameters, enabling extraordinary capabilities across diverse domains. However, these achievements come at a significant cost in terms of memory and energy consumption. The growing demand for computational resources raises grand challenges for the sustainable development of energy-efficient AI systems. This paper delves into the paradigm of memory-based computing as a promising avenue to address these challenges. By capitalizing on the inherent characteristics of memory and its efficient utilization, memory-based computing offers a novel approach to enhance AI performance while reducing the associated energy costs. Our paper systematically analyzes the multifaceted aspects of this paradigm, highlighting its potential benefits and outlining the challenges it poses. Through an exploration of various methodologies, architectures, and algorithms, we elucidate the intricate interplay between memory utilization, computational efficiency, and AI model complexity. Furthermore, we review the evolving area of hardware and software solutions for memory-based computing, underscoring their implications for achieving energy-efficient AI systems. As AI continues its rapid evolution, identifying the key challenges and insights presented in this paper serve as a foundational guide for researchers striving to navigate the complex field of memory-based computing and its pivotal role in shaping the future of energy-efficient AI. 

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5. Distributed Speed Scaling in Large-Scale Service Systems

   https://doi.org/10.1287/opre.2024.1012  

   Rutten, Daan; Zubeldia, Martin; Mukherjee, Debankur (June 2025, Operations Research)

   Smart Servers, Smarter Speed Scaling: A Decentralized Algorithm for Data Center Efficiency A team of researchers from Georgia Tech and the University of Minnesota has introduced a cutting-edge algorithm designed to optimize energy use in large-scale data centers. As detailed in their paper “Distributed Rate Scaling in Large-Scale Service Systems,” the team developed a decentralized method allowing each server to adjust its processing speed autonomously without the need for communication or knowledge of system-wide traffic. The algorithm uses idle time as a local signal to guide processing speed, ensuring that all servers converge toward a globally optimal performance rate. This innovation addresses a critical issue in modern computing infrastructure: balancing energy efficiency with performance under uncertainty and scale. The authors demonstrate that their approach not only stabilizes the system but achieves asymptotic optimality as the number of servers increases. The work is poised to significantly reduce energy consumption in data centers, which are projected to account for up to 8% of U.S. electricity use by 2030. 

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