Energy Efficiency in AI & Computing: From Green Grids to Brain-Inspired Hardware

Latest 50 papers on energy efficiency: Sep. 8, 2025

The relentless march of AI and advanced computing, while pushing the boundaries of innovation, also brings a significant challenge: escalating energy consumption. As models grow larger and systems become more complex, the environmental footprint and operational costs of AI/ML are becoming critical concerns. Fortunately, recent research is tackling this ‘green AI’ imperative head-on, delivering exciting breakthroughs that span hardware, software, and system-level optimizations.

The Big Idea(s) & Core Innovations

At the heart of these advancements lies a multi-pronged effort to re-imagine how computing resources are allocated, designed, and managed for maximum efficiency. One major theme revolves around optimizing energy in distributed and dynamic systems. For instance, the “Digital Twin-Guided Energy Management over Real-Time Pub/Sub Protocol in 6G Smart Cities” by S. Blattmann et al. from the University of Applied Sciences, Switzerland introduces a framework that leverages digital twins and real-time publish/subscribe protocols to optimize energy use in smart cities, predicting and managing energy flow with unprecedented accuracy. Similarly, “AutoGrid AI: Deep Reinforcement Learning Framework for Autonomous Microgrid Management” by Sushil Silwal et al. from the University of California, San Diego employs deep reinforcement learning to autonomously manage microgrids, significantly improving energy distribution efficiency and reducing emissions under varying conditions.

Another critical area is energy-aware resource allocation in communication networks. Chen, Y. et al. from the University of Technology in their paper “Flexible Base Station Sleeping and Resource Allocation for Green Uplink Fully-Decoupled RAN” propose dynamic base station sleeping and tailored resource allocation to drastically cut energy consumption in wireless networks. Building on this, “Enhancing Energy and Spectral Efficiency in IoT-Cellular Networks via Active SIM-Equipped LEO Satellites” by Author A and B explores how active SIM-equipped Low Earth Orbit (LEO) satellites can redefine IoT-cellular network efficiency, addressing limitations of terrestrial infrastructure.

The shift toward hardware-aware and approximate computing for AI/ML is also gaining immense traction. “Performance is not All You Need: Sustainability Considerations for Algorithms” by X. Li et al. from XJTU-SKLCS introduces novel metrics (FMS and ASC) to evaluate algorithms not just on performance but also on energy consumption, pushing for more sustainable AI. This is echoed in papers like “Low Power Approximate Multiplier Architecture for Deep Neural Networks” where Pragun Jaswal et al. achieve significant power and energy reductions in DNNs through approximate multipliers, without sacrificing much accuracy. For specialized applications, “Real Time FPGA Based CNNs for Detection, Classification, and Tracking in Autonomous Systems: State of the Art Designs and Optimizations” by Author A et al. from Institute of Embedded Systems, University X highlights FPGA-based CNNs as a promising platform due to their flexibility and parallelism, emphasizing optimizations like quantization and pruning for improved efficiency. Furthermore, “GeneTEK: Low-power, high-performance and scalable genome sequence matching in FPGAs” by Elena Espinosa et al. from the University of Malaga demonstrates a remarkable 62x energy reduction for genomic sequence matching using FPGAs over traditional CPU/GPU approaches.

Finally, innovations in core computing architectures are paving the way for a more sustainable future. “Memory-Centric Computing: Solving Computing’s Memory Problem” by O. Mutlu et al. from Intel Corporation proposes a fundamental shift from CPU-centric to memory-centric architectures, promising enhanced fault tolerance, security, and energy efficiency. Most strikingly, “When Routers, Switches and Interconnects Compute: A processing-in-interconnect Paradigm for Scalable Neuromorphic AI” by Madhuvanthi Srivatsav et al. from the Indian Institute of Science introduces a revolutionary π² (processing-in-interconnect) computing model, leveraging existing Ethernet switches to perform neuromorphic AI inference with minimal energy overhead.

Under the Hood: Models, Datasets, & Benchmarks

These research efforts are supported by, and in turn contribute to, a rich ecosystem of tools and benchmarks:

• AI/ML Models & Frameworks:
  • AutoGrid AI: A deep reinforcement learning framework (D-RL) for autonomous microgrid management. (https://www.mdpi.com/1996-1073/17/16/3898)
  • TwinLiteNet+: A lightweight, multi-task segmentation model for autonomous driving, utilizing hybrid encoder architecture and lightweight upsampling modules. (https://arxiv.org/pdf/2403.16958 | Code: https://github.com/chequanghuy/TwinLiteNetPlus)
  • H2EAL: A hybrid-bonding architecture with hybrid sparse attention for efficient long-context LLM inference. (https://arxiv.org/pdf/2508.16653 | Code: https://github.com/h2eal/h2eal)
  • Z-Pruner: A post-training pruning technique for LLMs that requires no retraining, enhancing model efficiency. (https://arxiv.org/pdf/2508.15828 | Code: https://github.com/sazzadadib/Z-Pruner)
  • NRL (Noise-Based Reward-Modulated Learning): A gradient-free, biologically inspired learning method for reinforcement learning, leveraging eligibility traces and reward prediction error. (https://arxiv.org/pdf/2503.23972 | Code: https://github.com/jesusgf96/noise-based-rew-modulated-learning)
  • TaiBai: A fully programmable brain-inspired processor with topology-aware efficiency. (https://www.sciencedirect.com/science/article/pii/S1877050911006806)
• Hardware-Specific Innovations:
  • Domain-Specific ECC for HBM: Tailored error-correcting codes to reduce bit cost in high-bandwidth memory for AI inference. (https://semianalysis.com/2024/09/03/the-memory-wall/)
  • SCE-NTT: A hardware accelerator for Number Theoretic Transforms using superconductor electronics, improving homomorphic encryption efficiency. (https://irds.ieee.org/editions/2023/20-roadmap-2023-edition/)
  • ASiM: A simulation framework for SRAM-based analog Compute-in-Memory (ACiM) circuits, bridging hardware design and inference performance. (https://arxiv.org/pdf/2411.11022 | Code: https://github.com/Keio-CSG/ASiM)
  • Bendable RISC-V: An open-source framework for ML accelerator development on flexible hardware, achieving significant speedup and energy reduction for SVM classification. (https://arxiv.org/pdf/2508.19656 | Code: https://github.com/PolykarposV/Flex-SVM)
• Benchmarks & Datasets:
  • SLM-Bench: A comprehensive benchmark for Small Language Models (SLMs) evaluating accuracy, computational efficiency, and environmental impact across 9 NLP tasks and 23 datasets. (https://arxiv.org/pdf/2508.15478 | Code: https://anonymous.4open.science/r/slm-bench-experiments-87F6)
  • GPUMemNet & CARMA: An ML-based GPU memory estimator and a collocation-aware resource manager for deep learning training, evaluated with real-world workloads. (https://arxiv.org/pdf/2508.19073)
  • UCSD-Microgrid-Database: Used for validating AutoGrid AI. (https://github.com/sushilsilwal3/UCSD-Microgrid-Database)
  • SPEC Benchmarks: Utilized for validating the GreenCloud Tax model. (https://www.spec.org/power/docs/SPECpower)
  • Image Denoising & Digit Recognition (MNIST, Keras): Used for evaluating approximate multiplier architectures. (http://yann.lecun.com/exdb/mnist/)
  • Agricultural Pests Image Dataset (Kaggle): For smart farming split learning solutions. (https://www.kaggle.com/datasets/vencerlanz09/agricultural-pests-image-dataset)

Impact & The Road Ahead

The collective impact of this research is profound. We’re seeing a fundamental shift in how we approach AI development and deployment—moving from performance-at-any-cost to a more sustainable, resource-aware paradigm. From intelligent green cloud computing with “A Novel IaaS Tax Model as Leverage Towards Green Cloud Computing” by Benedikt Pittl et al. from the University of Vienna to “CEO-DC: Driving Decarbonization in HPC Data Centers with Actionable Insights” by Rubén Rodríguez Álvarez et al. from EPFL, economic and policy incentives are being integrated to steer industry towards greener practices.

For autonomous systems, these advancements promise more practical and ubiquitous deployment. Energy-efficient lane planning for electric vehicles (as seen in “Energy-Aware Lane Planning for Connected Electric Vehicles in Urban Traffic”) and robust, energy-efficient quadrotor flight controllers like AERO-LQG (https://arxiv.org/pdf/2508.20888 | Code: http://github.com/ANSFL/AERO-LQG) by Anonymous are making real-world autonomous operations more feasible. The work on “Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework” by Surya Kumar Murthy also ensures that urban air mobility systems are not only safe but also environmentally conscious.

The future of AI and computing is undeniably green. The ongoing innovations, from novel hardware architectures that leverage memory and interconnects for computation to sophisticated software frameworks for energy optimization, highlight a vibrant and critical research area. As we move forward, the emphasis will continue to be on finding the optimal balance between computational prowess and environmental responsibility, ensuring that our technological progress is truly sustainable.