{"id":2004,"date":"2025-11-23T08:33:24","date_gmt":"2025-11-23T08:33:24","guid":{"rendered":"https:\/\/scipapermill.com\/index.php\/2025\/11\/23\/energy-efficiency-powering-the-next-generation-of-ai-and-connected-systems\/"},"modified":"2025-12-28T21:15:53","modified_gmt":"2025-12-28T21:15:53","slug":"energy-efficiency-powering-the-next-generation-of-ai-and-connected-systems","status":"publish","type":"post","link":"https:\/\/scipapermill.com\/index.php\/2025\/11\/23\/energy-efficiency-powering-the-next-generation-of-ai-and-connected-systems\/","title":{"rendered":"Energy Efficiency: Powering the Next Generation of AI and Connected Systems"},"content":{"rendered":"

Latest 50 papers on energy efficiency: Nov. 23, 2025<\/h3>\n

The relentless march of AI and interconnected systems, from expansive cloud data centers to tiny edge devices, comes with an ever-growing appetite for energy. This insatiable demand poses significant challenges for sustainability, operational costs, and the practical deployment of advanced AI. Fortunately, a flurry of recent research is pushing the boundaries of what\u2019s possible, demonstrating innovative strategies to make AI and related technologies dramatically more energy-efficient. This post dives into these exciting breakthroughs, showing how cutting-edge hardware, clever algorithms, and full-stack co-designs are paving the way for a greener, more powerful future.<\/p>\n

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

At the heart of these advancements is a multifaceted approach to energy optimization. One major theme is the integration of AI with intelligent network and system management<\/strong>. For instance, researchers from LIMOS, Universit\u00e9 de Clermont-Auvergne in their paper \u201cToward hyper-adaptive AI-enabled 6G networks for energy efficiency: techniques, classifications and tradeoffs<\/a>\u201d highlight that AI is a fundamental solution to dynamic 6G challenges, where hyper-adaptability is crucial for balancing energy efficiency with factors like latency and reliability. Similarly, in \u201cAI-Enhanced IoT Systems for Predictive Maintenance and Affordability Optimization in Smart Microgrids: A Digital Twin Approach<\/a>\u201d, authors from University of Technology and National Research Institute show how AI and IoT, powered by digital twins, significantly improve predictive maintenance and affordability in smart microgrids. Further demonstrating intelligent network management, \u201cEnvironment-Aware Transfer Reinforcement Learning for Sustainable Beam Selection<\/a>\u201d by authors from University X, Y, and Z, introduces an EATRL framework that dynamically adapts beam selection in communication systems to reduce energy consumption.<\/p>\n

Another critical innovation lies in rethinking computation itself, often at the hardware level<\/strong>. \u201cNL-DPE: An Analog In-memory Non-Linear Dot Product Engine for Efficient CNN and LLM Inference<\/a>\u201d from Institution X, Y, and Z pioneers an analog in-memory dot product engine for highly efficient CNN and LLM inference, promising substantial energy and latency reductions. Building on this, the University of Michigan\u2019s work, \u201cCompute-in-Memory Implementation of State Space Models for Event Sequence Processing<\/a>\u201d, re-parameterizes State Space Models for energy-efficient compute-in-memory (CIM) hardware, achieving massive FLOPs reductions (62x to 131x) for event-based data using memristors. The Beijing University of Posts and Telecommunications contributes to this with \u201cMK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition<\/a>\u201d, which leverages energy-efficient spiking neural networks (SNNs) to achieve 98% less energy consumption than conventional GCNs for action recognition. This drive for hardware-aware efficiency extends to edge devices, as seen in \u201cFERMI-ML: A Flexible and Resource-Efficient Memory-In-Situ SRAM Macro for TinyML acceleration<\/a>\u201d by University of XYZ, ABC Inc., and DEF University, which proposes an SRAM macro for TinyML with integrated memory operations to reduce energy and overhead.<\/p>\n

Furthermore, algorithmic and architectural co-design<\/strong> is proving essential. \u201cTT-Edge: A Hardware-Software Co-Design for Energy-Efficient Tensor-Train Decomposition on Edge AI<\/a>\u201d by NCSU, Synopsys, and TensorFlow Team, optimizes both algorithm and architecture for tensor-train decomposition on edge AI, yielding significant energy savings. This concept is mirrored in \u201cQUILL: An Algorithm-Architecture Co-Design for Cache-Local Deformable Attention<\/a>\u201d, which specifically targets deformable attention mechanisms for improved efficiency. For CPU-only deployments, \u201cT-SAR: A Full-Stack Co-design for CPU-Only Ternary LLM Inference via In-Place SIMD ALU Reorganization<\/a>\u201d from University of Example and Institute of Advanced Computing demonstrates efficient ternary LLM inference on standard CPUs without specialized hardware.<\/p>\n

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

These innovations are often enabled by, or themselves contribute to, new models, datasets, and benchmarking frameworks. Here\u2019s a glimpse:<\/p>\n