{"id":1335,"date":"2025-09-29T07:59:45","date_gmt":"2025-09-29T07:59:45","guid":{"rendered":"https:\/\/scipapermill.com\/index.php\/2025\/09\/29\/energy-efficiency-in-ai-ml-from-edge-to-cloud-and-beyond\/"},"modified":"2025-12-28T22:04:54","modified_gmt":"2025-12-28T22:04:54","slug":"energy-efficiency-in-ai-ml-from-edge-to-cloud-and-beyond","status":"publish","type":"post","link":"https:\/\/scipapermill.com\/index.php\/2025\/09\/29\/energy-efficiency-in-ai-ml-from-edge-to-cloud-and-beyond\/","title":{"rendered":"Energy Efficiency in AI\/ML: From Edge to Cloud and Beyond"},"content":{"rendered":"

Latest 50 papers on energy efficiency: Sep. 29, 2025<\/h3>\n

The relentless march of AI and Machine Learning has brought unprecedented capabilities, but it\u2019s also ushered in a growing concern: energy consumption. Training and deploying complex models, especially large language models (LLMs) and deep neural networks (DNNs), demand significant computational resources, leading to substantial energy footprints. The good news? Recent research is vigorously tackling this challenge, exploring innovative solutions spanning hardware, software, and algorithmic advancements. This digest dives into some of the latest breakthroughs, offering a glimpse into a more sustainable AI future.<\/p>\n

The Big Idea(s) & Core Innovations<\/h3>\n

At the heart of many recent advancements is the idea of optimizing computations where they happen<\/strong>, whether that\u2019s at the edge, in specialized hardware, or through smarter network communication. A standout theme is the move towards neuromorphic computing and spiking neural networks (SNNs)<\/strong>, which intrinsically mimic the energy-efficient processing of the brain. Papers like \u201cNeuromorphic Intelligence\u201d<\/a> by Marcel van Gerven (Donders Institute, Radboud University) propose dynamical systems theory as a unifying framework, advocating for evolutionary algorithms and noise-based learning to achieve emergent intelligence sustainably. Building on this, \u201cIncorporating the Refractory Period into Spiking Neural Networks through Spike-Triggered Threshold Dynamics\u201d<\/a> from authors including Yang Li and Yan Wang (Sichuan University) introduces RPLIF, a novel SNN model that enhances robustness and performance by incorporating biological refractory periods, significantly improving efficiency with minimal overhead. Further demonstrating SNN\u2019s potential, \u201cDendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling\u201d<\/a> by Dehao Zhang et al.\u00a0(University of Electronic Science and Technology of China) unveils the D-RF neuron, using multi-dendritic structures and adaptive thresholds for efficient long sequence modeling. And in audio, \u201cSpiking Vocos: An Energy-Efficient Neural Vocoder\u201d<\/a> by Yukun Chen et al.\u00a0(Xi\u2019an Jiaotong University) achieves high-quality audio synthesis with a mere 14.7% of the energy consumed by traditional ANNs.<\/p>\n

Another critical innovation focuses on hardware acceleration and memory-centric computing<\/strong>. For instance, \u201cLEAP: LLM Inference on Scalable PIM-NoC Architecture with Balanced Dataflow and Fine-Grained Parallelism\u201d<\/a> by P.-Y. Chen et al.\u00a0(Meta AI Research, University of California, Berkeley) proposes a novel architecture combining Processing-in-Memory (PIM) with Network-on-Chip (NoC) for dramatically more efficient LLM inference. Similarly, \u201cCompAir: Synergizing Complementary PIMs and In-Transit NoC Computation for Efficient LLM Acceleration\u201d<\/a> by Hongyi Li et al.\u00a0(Tsinghua University) introduces a hybrid PIM architecture that performs non-linear operations during data movement to cut communication overhead and energy. For edge AI, \u201cTENET: An Efficient Sparsity-Aware LUT-Centric Architecture for Ternary LLM Inference On Edge\u201d<\/a> from researchers including Author A (Institute of Advanced Computing, University X) shows an architecture delivering up to 21.1x higher energy efficiency than an A100 GPU for ternary LLM inference. Further hardware optimization for FPGAs is explored in \u201cHolistic Optimization Framework for FPGA Accelerators\u201d<\/a> (Prometheus) by St\u00e9phane Pouget et al.\u00a0(UCLA), which automates design space exploration, and \u201cSpecMamba: Accelerating Mamba Inference on FPGA with Speculative Decoding\u201d<\/a> by N. Goyal et al.\u00a0(Meta AI, Google Brain) uses speculative decoding for faster Mamba inference.<\/p>\n

Beyond hardware, smarter algorithms and frameworks<\/strong> are making waves. \u201cPagoda: An Energy and Time Roofline Study for DNN Workloads on Edge Accelerators\u201d<\/a> by Prashanthi S. K. et al.\u00a0(Indian Institute of Science) reveals that optimizing for time often implies optimizing for energy, challenging conventional wisdom in edge DNN deployment. In the realm of networking, papers such as \u201cGLo-MAPPO: A Multi-Agent Proximal Policy Optimization for Energy Efficiency in UAV-Assisted LoRa Networks\u201d<\/a> by Author Name 1 et al.\u00a0(Institute of Advanced Technology, University X) and \u201cFederated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks\u201d<\/a> by Minghao Chen et al.\u00a0(University of Ottawa) leverage multi-agent reinforcement learning for energy-efficient resource management, especially in dynamic 6G and UAV-assisted scenarios. Even traditional systems are seeing gains: \u201cEnergy saving in off-road vehicles using leakage compensation technique\u201d<\/a> by Gyan Wrat and J. Das (Aalborg University, IIT(ISM), Dhanbad) achieves an 8.54% energy reduction in hydraulic systems using a flow control valve strategy.<\/p>\n

Under the Hood: Models, Datasets, & Benchmarks<\/h3>\n

This wave of research introduces or leverages a suite of powerful tools and methodologies:<\/p>\n