Energy Efficiency in AI & Beyond: A Revolution in Sustainable Computing

Latest 50 papers on energy efficiency: Sep. 14, 2025

The relentless march of AI and advanced computing, while transformative, comes with a growing appetite for energy. From powering massive language models to operating fleets of autonomous robots and complex communication networks, the environmental footprint of our digital future is a pressing concern. But fear not, for a wave of innovative research is charting a course towards a greener, more sustainable technological landscape. This digest dives into recent breakthroughs that are redefining energy efficiency across diverse AI and computing domains.

The Big Idea(s) & Core Innovations

At the heart of these advancements lies a common thread: optimizing resource utilization, whether it’s computation, memory, or network bandwidth, to dramatically reduce energy consumption. In the realm of large language models (LLMs), a significant focus is on making inference more efficient. Papers like Benchmarking Energy Efficiency of Large Language Models Using vLLM by researchers including those from OpenAI and NVIDIA, highlight how frameworks like vLLM can slash energy consumption by optimizing memory and computation during LLM inference. Further pushing the boundaries, Wenlun Zhang from the University of California, Berkeley introduces BitROM: Weight Reload-Free CiROM Architecture Towards Billion-Parameter 1.58-bit LLM Inference, which uses compute-in-memory (CiROM) and low-bit precision to reduce DRAM access by 43.6%, enabling efficient LLM deployment on edge devices. Similarly, Angery Bob from University of [Unknown] proposes HD-MoE: Hybrid and Dynamic Parallelism for Mixture-of-Expert LLMs with 3D Near-Memory Processing, a framework for MoE models that combines hybrid and dynamic parallelism with near-memory processing for superior efficiency and scalability.

Beyond LLMs, the quest for efficiency extends to hardware and distributed systems. The paper Characterizing and Optimizing Realistic Workloads on a Commercial Compute-in-SRAM Device by Niansong Zhang et al. from Cornell University and GSI Technology Inc. demonstrates that compute-in-SRAM can match GPU performance with better energy efficiency through smart data movement optimizations. For specialized AI tasks, Ismael Gomez and Guangzhi Tang from ERNIS-LAB present Full Integer Arithmetic Online Training for Spiking Neural Networks, enabling SNN training with significantly reduced computational and memory overhead using only integer arithmetic. In distributed systems, Arefin Niam et al. from Tennessee Technological University introduce RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks, which dramatically cuts communication overhead and energy use in GNN training via adaptive caching and prefetching.

The push for sustainability isn’t just about raw computational power but also about holistic system design. Shvetank Prakash et al. from Harvard University and Pragmatic Semiconductor introduce Lifetime-Aware Design of Item-Level Intelligence, a framework that optimizes the carbon footprint of disposable flexible electronics by a remarkable 14.5x. For robust AI in challenging environments, X. Wang et al. present Variance-Aware Noisy Training: Hardening DNNs against Unstable Analog Computations, a novel training procedure that significantly enhances DNN robustness (up to 99.7% on Tiny ImageNet) for energy-efficient analog computing by explicitly modeling temporal noise variations. Even in robotic control, Filip Bjelonic et al. from ETH Zurich’s Robotics and Embedded Systems Lab (RESL) achieve significant energy savings in legged robots with Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots, integrating physics-based energy models to reduce the Cost of Transport by 32% on platforms like ANYMAL.

Under the Hood: Models, Datasets, & Benchmarks

These research efforts are underpinned by innovative models, specialized datasets, and rigorous benchmarking frameworks:

• vLLM Framework for LLM Benchmarking: Utilized in Benchmarking Energy Efficiency of Large Language Models Using vLLM, this framework offers a comprehensive approach to assess and optimize LLM performance under real-world constraints, emphasizing the trade-offs between computational cost, latency, and energy consumption. Public code is often associated with vLLM, encouraging further exploration.
• HD-MoE Framework and Dynamic Expert Placement: Introduced in HD-MoE: Hybrid and Dynamic Parallelism for Mixture-of-Expert LLMs with 3D Near-Memory Processing, this framework and its online dynamic expert placement strategy are crucial for optimizing MoE models. Code for HD-MoE is available at https://github.com/angerybob/HD-MoE.
• BitROM Architecture with eDRAM/ROM: The core innovation in BitROM: Weight Reload-Free CiROM Architecture Towards Billion-Parameter 1.58-bit LLM Inference enables highly efficient LLM inference on edge devices, with resources available at https://github.com/Wenlun.
• Compute-in-SRAM Analytical Framework: Developed in Characterizing and Optimizing Realistic Workloads on a Commercial Compute-in-SRAM Device, this framework helps identify optimization opportunities on commercial compute-in-SRAM devices, with code for a Phoenix simulator available at https://github.com/kozyraki/phoenix.
• Variance-Aware Noisy Training (VANT): Proposed in Variance-Aware Noisy Training: Hardening DNNs against Unstable Analog Computations, VANT is a novel training procedure that accounts for temporal variations in hardware noise. The code repository for VANT is accessible at https://github.com/HAWAIILAB/VANT.
• FlexiFlow Framework, FlexiBench, and FlexiBits: From Lifetime-Aware Design of Item-Level Intelligence, FlexiFlow provides a lifetime-aware design for item-level intelligence, supported by the FlexiBench benchmark suite and FlexiBits RISC-V microprocessors. Code is available at https://github.com/PragmaticSemiconductor/FlexiFlow.
• GreenDFL Framework and Algorithms: GreenDFL: a Framework for Assessing the Sustainability of Decentralized Federated Learning Systems introduces GreenDFL for quantifying energy and carbon emissions in DFL, along with GreenDFL-SA and GreenDFL-SN algorithms for sustainable aggregation and node selection. Code can be found at https://github.com/CyberDataLab/nebula.
• AutoGrid AI Framework for Microgrid Management: Presented in AutoGrid AI: Deep Reinforcement Learning Framework for Autonomous Microgrid Management, this DRL framework optimizes energy distribution and reduces emissions in microgrids, often validated with real-world datasets like the UCSD Microgrid Database. Relevant code is available at https://github.com/sushilsilwal3/UCSD-Microgrid-Database.
• Greener Deep Reinforcement Learning Metrics: The paper Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks introduces FMS (F-Measure on Sustainability) and ASC (Area under Sustainability Curve) to evaluate algorithms based on energy and carbon footprints, with code available via Stable Baselines 3 documentation.
• GeneTEK FPGA-based Accelerator: For genomics, GeneTEK: Low-power, high-performance and scalable genome sequence matching in FPGAs uses a flexible, worker-based FPGA architecture to implement Myers’s algorithm, achieving impressive energy reductions.
• Hybrid DRL-LLM Approach for UAVs: Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness introduces a novel algorithm combining DRL with LLMs for robust UAV planning. Code is available at https://github.com/BJTU-STIC/.
• PCPD Search Framework for Robotics: Energy-Efficient Path Planning with Multi-Location Object Pickup for Mobile Robots on Uneven Terrain introduces the concurrent PCPD search for optimizing path and object pickup, adapted from Compressed Path Database (CPD) techniques.

Impact & The Road Ahead

The collective impact of this research is profound, painting a picture of an AI/ML future that is not just powerful but also inherently sustainable. From significantly reducing the carbon footprint of massive AI models to enabling energy-efficient robotics in real-world scenarios, these innovations are critical for widespread, responsible deployment.

Imagine smart homes in New Zealand (as explored in Prototyping an AI-powered Tool for Energy Efficiency in New Zealand Homes by Abdollah Baghaei Daemei from Tech Innovation Experts) leveraging AI to guide homeowners toward cost-effective retrofits, or disaster response drone swarms making real-time, energy-efficient decisions powered by human-LLM synergy (Human-LLM Synergy in Context-Aware Adaptive Architecture for Scalable Drone Swarm Operation by Ahmed R. Sadik et al. from Honda Research Institute Europe). We’re also seeing advancements in core infrastructure, like memory-centric computing paradigms (Memory-Centric Computing: Solving Computing’s Memory Problem by O. Mutlu et al. from Intel Corporation and CMU-SAFARI Research Group) and domain-specific ECC for HBM (Breaking the HBM Bit Cost Barrier: Domain-Specific ECC for AI Inference Infrastructure by J. Koch et al. from SemiAnalysis), that promise foundational shifts toward greener hardware.

The road ahead involves continued research into hybrid electronic-photonic AI systems (Toward Lifelong-Sustainable Electronic-Photonic AI Systems via Extreme Efficiency, Reconfigurability, and Robustness), refining metrics for sustainable algorithm design (Performance is not All You Need: Sustainability Considerations for Algorithms by X. Li et al. from XJTU-SKLCS), and integrating these innovations into next-generation networks like 6G (TREE: Token-Responsive Energy Efficiency Framework For Green AI-Integrated 6G Networks by Author A et al. from University of Example). The challenge of energy efficiency in AI is being met with remarkable ingenuity, paving the way for a future where powerful intelligence coexists harmoniously with a healthy planet.