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1. 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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2. A Vision for Computational Decarbonization of Societal Infrastructure

   https://doi.org/10.1109/MIC.2025.3575016  

   Irwin, David; Shenoy, Prashant; Hajiesmaili, Mohammad; Hanafy, Walid A; Oke, Jimi; Sitaraman, Ramesh; Agarwal, Yuvraj; Gordon, Geoffrey J; Kolter, Zico; Rajagopal, Deepak; et al (March 2025, IEEE Internet Computing)

   Modern society is at a critical inflection point with rapidly accelerating demand for energy due to growth in domestic manufacturing, datacenters, artificial intelligence (AI), electric vehicles, and electric heat pumps. Sustaining this growth while also reducing society’s carbon emissions will necessitate a shift beyond our long-standing focus on improving energy efficiency to optimizing carbon efficiency. This paper lays out a vision for a new field of computational decarbonization, which focuses on optimizing and reducing the lifecycle carbon emissions of complex computing and societal infrastructure systems. We identify an important class of decarbonization problems that arise from interdependencies across multiple infrastructure domains, including computing, transportation, the built environment, and the electric power grid. As we discuss, solving these problems will require developing novel computational techniques, algorithms, systems, and AI methods that sense, optimize, and reduce the operational, embodied, and lifecycle greenhouse gas emissions of societal infrastructure over long temporal and spatial scales. 

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3. 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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4. Edge Cloud Offloading Algorithms: Issues, Methods, and Perspectives

   Wang, Jianyu; Pan, Jianli; Esposito, Flavio; Calyam, Prasad; Yang, Zhicheng; Mohapatra, Prasant. (October 2018, ACM computing surveys)

   Mobile devices supporting the "Internet of Things" (IoT), often have limited capabilities in computation, battery energy, and storage space, especially to support resource-intensive applications involving virtual reality (VR), augmented reality (AR), multimedia delivery and artificial intelligence (AI), which could require broad bandwidth, low response latency and large computational power. Edge cloud or edge computing is an emerging topic and technology that can tackle the deficiency of the currently centralized-only cloud computing model and move the computation and storage resource closer to the devices in support of the above-mentioned applications. To make this happen, efficient coordination mechanisms and “offloading” algorithms are needed to allow the mobile devices and the edge cloud to work together smoothly. In this survey paper, we investigate the key issues, methods, and various state-of-the-art efforts related to the offloading problem. We adopt a new characterizing model to study the whole process of offloading from mobile devices to the edge cloud. Through comprehensive discussions, we aim to draw an overall “big picture” on the existing efforts and research directions. Our study also indicates that the offloading algorithms in edge cloud have demonstrated profound potentials for future technology and application development. 

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5. Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

   https://doi.org/10.1186/s40537-020-00361-2  

   Huerta, E. A.; Khan, Asad; Davis, Edward; Bushell, Colleen; Gropp, William D.; Katz, Daniel S.; Kindratenko, Volodymyr; Koric, Seid; Kramer, William T.; McGinty, Brendan; et al (December 2020, Journal of Big Data)

   Abstract Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry. 

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