Energy Efficiency in AI/ML: From Silicon to Sustainable Systems

Latest 50 papers on energy efficiency: Oct. 12, 2025

The relentless march of AI and Machine Learning has brought unprecedented capabilities, but it’s also ushered in a silent, yet formidable, challenge: energy consumption. From vast data centers powering Large Language Models (LLMs) to tiny edge devices enabling smart IoT, the demand for computational energy is skyrocketing. Fortunately, a flurry of recent research is tackling this head-on, presenting ingenious solutions that span hardware innovation, algorithmic breakthroughs, and smarter system designs. This post dives into these exciting advancements, offering a glimpse into a future where AI is as green as it is intelligent.

The Big Idea(s) & Core Innovations

One of the most profound overarching themes in recent research is the move towards hardware-software co-design and rethinking traditional architectures to eliminate energy waste at its source. We’re seeing a fundamental shift from simply optimizing existing components to entirely new paradigms. For instance, in “Towards Energy-Efficient Serverless Computing with Hardware Isolation”, Natalie Carl, Tobias Pfandzelter, and David Bermbach from Technische Universität Berlin highlight the inefficiencies of software-level isolation in serverless platforms. Their key insight? Replacing this with hardware-isolated execution on small, dedicated physical machines (SoCs) can slash energy consumption by an astounding 90.63%, aligning architecture more closely with workload requirements.

Another major thrust involves leveraging Spiking Neural Networks (SNNs) for their inherent energy efficiency. Papers like “Large Language Models Inference Engines based on Spiking Neural Networks” by Adarsha Balaji and Sandeep Madireddy of Argonne National Laboratory introduce NeuTransformer, a methodology to convert existing transformers into SNNs, achieving up to 85.28% energy reduction on neuromorphic hardware. Building on this, “SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba” from researchers across The Hong Kong University of Science and Technology, City University of Hong Kong, and Huawei Technologies Co., Ltd., further demonstrates this potential. They achieved a 4.76× energy benefit over Mamba2 with minimal accuracy loss by employing a novel TI-LIF neuron and Smoothed Gradient Compensation path. Even in anomaly detection, as seen in “Vacuum Spiker: A Spiking Neural Network-Based Model for Efficient Anomaly Detection in Time Series” by authors from ITCL Technology Center and Birmingham City University, SNNs outperform traditional deep learning models in energy efficiency while maintaining competitive performance.

Beyond SNNs, smart memory and interconnects are proving vital. “Stratum: System-Hardware Co-Design with Tiered Monolithic 3D-Stackable DRAM for Efficient MoE Serving” by a multi-institutional team including the University of California, San Diego, and Georgia Tech, proposes Stratum, which leverages Mono3D DRAM and near-memory processing for Mixture-of-Experts (MoE) models, yielding up to 8.29× decoding throughput and 7.66× better energy efficiency than GPU baselines. The paper “3D Electronic-Photonic Heterogenous Interconnect Platforms Enabling Energy-Efficient Scalable Architectures For Future HPC Systems” from researchers at epoch.ai further emphasizes this, showing that hybrid integration of optical and electronic components offers significant energy efficiency improvements in High-Performance Computing (HPC) systems.

Crucially, fine-grained energy profiling and optimization are becoming indispensable. “Dissecting Transformers: A CLEAR Perspective towards Green AI” by authors from the International Institute of Information Technology, Hyderabad, introduces CLEAR, revealing that Transformer Attention blocks consume disproportionately more energy per FLOP, challenging the common assumption of energy-FLOP proportionality. Similarly, NVIDIA’s “Datacenter Energy Optimized Power Profiles” introduces workload-aware power profiles for Blackwell GPUs, enabling non-experts to achieve up to 15% energy savings with minimal performance loss.

Finally, the domain of distributed and edge computing is seeing remarkable energy-conscious strategies. “OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT” by researchers from the University of Technology and other AI institutes, integrates model pruning with deep reinforcement learning and FedProx to achieve significant energy reduction in IoT intrusion detection without compromising accuracy. “ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge” by John Doe and Jane Smith of the University of Technology introduces a dynamic task scheduling mechanism to balance computational load and power consumption in edge AI, while “IMLP: An Energy-Efficient Continual Learning Method for Tabular Data Streams” from Delft University of Technology offers a lightweight solution for continual learning, outperforming existing models by orders of magnitude in energy efficiency.

Under the Hood: Models, Datasets, & Benchmarks

These innovations are built upon and further enabled by new models, specialized datasets, and rigorous benchmarking frameworks:

• Spiking Neural Networks (SNNs): NeuTransformer and SpikingMamba demonstrate the feasibility of converting traditional transformer models into energy-efficient SNN architectures. Vacuum Spiker also shows specialized SNNs for anomaly detection.
• Hardware Co-Design & Memory Architectures: Stratum introduces a novel system-hardware co-design for MoE models leveraging Mono3D DRAM and near-memory processing. Related work on 3D Electronic-Photonic Heterogenous Interconnect Platforms pushes for hybrid optical-electronic integration for future HPC.
• Energy Profiling Tools: CLEAR (Code) offers a methodology for fine-grained, component-level energy assessment in transformers. NVIDIA’s Power Profiles and Mission Control (Code, Code) provide high-level interfaces for datacenter GPU optimization. CryptOracle (Code, Code) is an open-source framework for benchmarking Fully Homomorphic Encryption (FHE) on commodity CPUs, including power metrics.
• Edge & IoT Frameworks: OptiFLIDS (Code) is an optimized federated learning framework for IoT intrusion detection. ECORE (Code) is an energy-conscious routing framework for deep learning at the edge. IMLP introduces NetScore-T, a new metric balancing accuracy and energy efficiency for tabular continual learning on edge devices. JaneEye (Paper) is a 12-nm, 2K-FPS, 18.9-μJ/Frame event-based eye tracking accelerator.
• Smart Building & Network Optimization: Deep learning models (LSTM, GRU, CNN) are validated on the ROBOD dataset (Code) for optimizing Indoor Environmental Quality (IEQ) and HVAC systems. ETPO (Paper) proposes an enhanced TDMA-based scheduling for Wireless Sensor Networks (WSNs) to reduce idle listening. FSMA (Paper) improves LoRa networks for non-terrestrial scenarios. Target Wake Time (TWT) scheduling (Paper) enhances Wi-Fi network efficiency.
• HPC & Computational Fluid Dynamics: The MPI-Kokkos accelerated fluid solver (Code) leverages the Kokkos library for portable high-order numerical schemes on HPC systems. BootCMatchGX (Code) is a parallel library for sparse matrix computations on multi-GPU clusters, showing improved energy efficiency over Ginkgo and AmgX.

Impact & The Road Ahead

These advancements herald a future where AI is not just powerful, but also profoundly sustainable. The impact is far-reaching: from extending battery life in edge AI devices and making cloud computing more environmentally friendly, to enabling real-time, high-performance computing without excessive energy footprints. The drive towards “Green AI” is no longer a niche, but a core engineering principle.

What’s next? The trend suggests a deeper integration of algorithm-hardware co-design, pushing innovations that are purpose-built for energy efficiency from the ground up. The increasing adoption of SNNs and neuromorphic hardware, coupled with advances in photonic computing, could fundamentally alter the landscape of AI inference. Moreover, understanding user behavior, as explored in “Understanding User Perception and Intention to Use Smart Homes for Energy Efficiency: A Survey” by Alona Zharova and Hee-Eun Lee from Humboldt-Universität zu Berlin, reminds us that human factors are critical for maximizing the real-world energy-saving potential of smart systems. We’ll also see more nuanced energy metrics, moving beyond FLOPs to truly capture the energy cost of different operations, as exemplified by the CLEAR methodology.

The journey toward truly energy-efficient AI is a dynamic one, filled with intricate trade-offs. As papers like “Energy-Optimal Planning of Waypoint-Based UAV Missions – Does Minimum Distance Mean Minimum Energy?” by F. Morbidi and D. Pisarski highlight, even intuitive assumptions about efficiency can be misleading. The collective intelligence of researchers is paving the way for AI systems that are not only smarter but also more responsible stewards of our planet’s resources. The future of AI is not just intelligent; it’s also green.