Energy Efficiency in AI: Powering the Future of Edge, Cloud, and Robotics

Latest 50 papers on energy efficiency: Oct. 20, 2025

The relentless march of AI innovation has brought incredible capabilities, from sophisticated language models to autonomous robotics. Yet, this progress comes with a significant and often overlooked cost: energy consumption. As AI permeates every facet of our lives, from smart homes to massive data centers, the demand for more sustainable, energy-efficient solutions has never been more critical. Recent research, spanning diverse fields from neuromorphic computing to hardware-software co-design, is paving the way for a greener, more powerful AI future. This digest explores some of the latest breakthroughs in this vital area.

The Big Idea(s) & Core Innovations

At the heart of these advancements is a multifaceted approach to energy conservation, tackling efficiency from the silicon up to network-wide orchestration. One major theme is the resurgence of Spiking Neural Networks (SNNs), which inherently promise lower power consumption by mimicking the sparse, event-driven communication of biological brains. For instance, the paper “SHaRe-SSM: An Oscillatory Spiking Neural Network for Target Variable Modeling in Long Sequences” by Kartikay Agrawal and colleagues from SustainAI Lab, IIT Guwahati, introduces a second-order spiking State Space Model (SSM) that outperforms first-order models on long sequences, making it ideal for energy-efficient edge AI. Similarly, “Vacuum Spiker: A Spiking Neural Network-Based Model for Efficient Anomaly Detection in Time Series” by I. X. Vázqueza et al. proposes a lightweight SNN for real-time anomaly detection, leveraging single-spike encoding and STDP-based training to achieve superior energy efficiency over traditional deep learning models. Further pushing SNN capabilities, “SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba” by Yulong Huang and co-authors from institutions like HKUST and Huawei, presents SpikingMamba, an SNN-based Large Language Model (LLM) that achieves an impressive 4.76x energy benefit over Mamba2 with minimal accuracy loss, a groundbreaking step for sustainable LLMs.

Beyond SNNs, hardware-software co-design emerges as a critical lever for energy efficiency. “RTGS: Real-Time 3D Gaussian Splatting SLAM via Multi-Level Redundancy Reduction” from the University of Minnesota and others introduces an algorithm-hardware co-design for 3D Gaussian Splatting (3DGS) SLAM on edge devices, achieving up to 82.5x energy efficiency gains. This is echoed in “LightMamba: Efficient Mamba Acceleration on FPGA with Quantization and Hardware Co-design”, where researchers including Skowron and L. Sutawika demonstrate a 1.43x speedup over GPU baselines for Mamba models on FPGAs, highlighting the power of specialized hardware. The paper “ReTiDe: Real-Time Denoising for Energy-Efficient Motion Picture Processing with FPGAs” by Changhong Li et al. from Trinity College Dublin also showcases FPGA’s potential, delivering 37.71x GOPS throughput and 5.29x higher energy efficiency for real-time denoising, a vital application in media production.

Data center and cloud optimization also sees significant innovation. Andrea Marinoni and colleagues from the University of Cambridge, Nyobolt Limited, and Nanyang Technological University, in “Improving AI Efficiency in Data Centres by Power Dynamic Response”, propose dynamic power management strategies that can significantly reduce energy consumption and operational costs in AI data centers. NVIDIA’s “Datacenter Energy Optimized Power Profiles” introduces a feature for Blackwell GPUs, enabling workload-aware optimization for up to 15% energy savings. For cloud-edge continuum, “QONNECT: A QoS-Aware Orchestration System for Distributed Kubernetes Clusters” presents a framework that automates microservice deployment with a QoS-aware scheduler, balancing energy, cost, and performance. Furthermore, “A Non-Intrusive Framework for Deferred Integration of Cloud Patterns in Energy-Efficient Data-Sharing Pipelines” by Sepideh Masoudi et al. from Technische Universität Berlin introduces SnapPattern, a Kubernetes-based tool for dynamically integrating cloud design patterns to improve data-sharing pipeline efficiency without modifying service code.

Even in specific domains like healthcare IoT and autonomous vehicles, energy efficiency is paramount. H. X. Son et al. from Ho Chi Minh City University of Technology in “SLIE: A Secure and Lightweight Cryptosystem for Data Sharing in IoT Healthcare Services” developed a cryptosystem that outperforms RSA by over 84% in encryption speed and 99% in decryption speed, making it highly energy-efficient for low-power medical IoT environments. For smart mobility, J. Paugh et al. from The Ohio State University, in “Traffic-Aware Eco-Driving Control in CAVs via Learning-based Terminal Cost Model”, introduce a neural network-based MPC framework for eco-driving in CAVs, achieving up to 6.5% energy efficiency improvements by integrating macroscopic traffic dynamics.

Under the Hood: Models, Datasets, & Benchmarks

These innovations are often driven by new models, datasets, and benchmarks that push the boundaries of energy-efficient AI:

• SpikingMamba: Introduced in “SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba”, this recurrent spiking LLM leverages a novel Ternary Integer Integrate-and-Fire (TI-LIF) neuron and a Smoothed Gradient Compensation (SGC) path for efficient inference. Code available at https://github.com/huawei-ai/spikingmamba.
• RTGS Framework: In “RTGS: Real-Time 3D Gaussian Splatting SLAM via Multi-Level Redundancy Reduction”, this algorithm-hardware co-design utilizes adaptive Gaussian pruning, dynamic downsampling, and a Gradient Merging Unit (GMU) for efficient 3DGS-SLAM. Code available at https://github.com/UMN-ZhaoLab/RTGS.
• LightMamba Accelerator: Presented in “LightMamba: Efficient Mamba Acceleration on FPGA with Quantization and Hardware Co-design”, this FPGA-based architecture uses quantization and computation reordering for Mamba models. Code is linked to https://github.com/PKU-SEC.
• SnapPattern: An open-source Kubernetes-based tool for non-intrusive, deferred integration of cloud design patterns in data-sharing pipelines, discussed in “A Non-Intrusive Framework for Deferred Integration of Cloud Patterns in Energy-Efficient Data-Sharing Pipelines”. Code at https://github.com/Sepide-Masoudi/SnapPattern.
• DUCEM Algorithm: From “Energy-efficient User Clustering for UAV-enabled Wireless Networks Using EM Algorithm”, DUCEM is a modified Expected-Maximization (EM) algorithm for efficient user clustering in UAV-enabled networks. Code at https://github.com/salimjanji/DUCEM.
• OptiFLIDS Framework: In “OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT”, this federated learning framework combines model pruning, adaptive scheduling, and FedProx for efficient IoT intrusion detection. Code at https://github.com/SAIDAELOUARDI23/OptiFLIDS-.git.
• ECORE Framework: Discussed in “ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge”, ECORE optimizes energy usage in edge computing by efficiently routing deep learning tasks. Code available at https://github.com/edge-ai-research/ecore.
• IMLP: In “IMLP: An Energy-Efficient Continual Learning Method for Tabular Data Streams”, IMLP is a context-aware incremental Multi-Layer Perceptron for energy-efficient continual learning on tabular data streams. It also introduces NetScore-T as a new metric for evaluating accuracy and energy efficiency.
• THEAS: Presented in “THEAS: Efficient Power Management in Multi-Core CPUs via Cache-Aware Resource Scheduling”, THEAS is a cache-aware resource scheduling framework for efficient power management in multi-core CPUs. (No public code found).
• SysLLMatic: From “SysLLMatic: Large Language Models are Software System Optimizers” by researchers at Carnegie Mellon University, this LLM-based framework integrates system performance knowledge and runtime feedback for software optimization.

Impact & The Road Ahead

These advancements herald a future where AI systems are not only powerful but also remarkably efficient, leading to substantial environmental and economic benefits. The widespread adoption of energy-efficient AI will enable more robust edge computing, making AI pervasive in resource-constrained IoT devices, medical applications, and autonomous systems. From optimizing traffic flow with eco-driving CAVs to enhancing data center sustainability, the real-world impact is immense.

The push toward SNNs, coupled with sophisticated hardware-software co-design, suggests a paradigm shift in how we build and deploy AI. As papers like “The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks” by Hoefler et al. remind us, while DNNs still hold sway, the dedicated pursuit of energy-efficient alternatives like SNNs, combined with hardware accelerators and smart orchestration, promises to unlock new frontiers. The next steps involve further refining these techniques, exploring new material science (like ferroelectric NAND flash in “FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash”), and developing standardized metrics and frameworks to seamlessly integrate these innovations across the AI ecosystem. The journey to truly sustainable and performant AI is well underway, and the research highlighted here is lighting the path forward.