{"id":1876,"date":"2025-11-16T10:24:18","date_gmt":"2025-11-16T10:24:18","guid":{"rendered":"https:\/\/scipapermill.com\/index.php\/2025\/11\/16\/energy-efficiency-in-ai-ml-from-silicon-to-sustainable-systems-2\/"},"modified":"2025-12-28T21:21:38","modified_gmt":"2025-12-28T21:21:38","slug":"energy-efficiency-in-ai-ml-from-silicon-to-sustainable-systems-2","status":"publish","type":"post","link":"https:\/\/scipapermill.com\/index.php\/2025\/11\/16\/energy-efficiency-in-ai-ml-from-silicon-to-sustainable-systems-2\/","title":{"rendered":"Energy Efficiency in AI\/ML: From Silicon to Sustainable Systems"},"content":{"rendered":"

Latest 50 papers on energy efficiency: Nov. 16, 2025<\/h3>\n

The relentless march of AI\/ML, while delivering unprecedented capabilities, comes with an increasingly significant environmental footprint. The sheer computational power required to train and run complex models is pushing the boundaries of energy consumption, making energy efficiency<\/em> a critical frontier. Fortunately, a flurry of recent research, as highlighted in the papers summarized here, is tackling this challenge head-on, from novel hardware designs to intelligent software orchestration and sustainable deployments.<\/p>\n

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

At the heart of these advancements lies a common theme: a fundamental shift towards hardware-software co-design<\/strong> and domain-specific optimizations<\/strong>. The paper \u201cThe Role of Advanced Computer Architectures in Accelerating Artificial Intelligence Workloads<\/a>\u201d emphasizes that AI is no longer merely an application running on hardware; it\u2019s an active partner in the design process. This symbiotic relationship is yielding groundbreaking results, as seen in the development of Processing-in-Memory (PIM)<\/strong> and neuromorphic computing<\/strong> architectures. A prime example is the event-driven spiking compute-in-memory macro based on SOT-MRAM, detailed in \u201cAn Event-Driven Spiking Compute-In-Memory Macro based on SOT-MRAM<\/a>\u201d, which significantly enhances energy efficiency for neuromorphic applications by processing asynchronous and sparse data more effectively. Similarly, the work on \u201cSelf-correcting High-speed Opto-electronic Probabilistic Computer<\/a>\u201d by Quantum Dice Limited, Oxford, UK, introduces a self-correcting optoelectronic probabilistic computer using quantum photonic p-bits, achieving remarkable speed and energy consumption figures by harnessing robust electronic control.<\/p>\n

Beyond specialized hardware, intelligent software-level optimizations<\/strong> are proving equally vital. \u201cAIM: Software and Hardware Co-design for Architecture-level IR-drop Mitigation in High-performance PIM<\/a>\u201d from Peking University and Houmo AI, introduces an innovative co-design approach that leverages workload characteristics to dynamically adjust power delivery, leading to substantial IR-drop mitigation and energy savings in PIM systems. In the realm of large language models (LLMs), \u201cLeveraging LLMs to Automate Energy-Aware Refactoring of Parallel Scientific Codes<\/a>\u201d by Matthew T. Dearing et al.\u00a0(University of Illinois Chicago and Argonne National Laboratory) presents LASSI-EE, an LLM-based framework that automates the generation of energy-efficient parallel scientific code, achieving up to 48% energy reduction across diverse hardware. This demonstrates how AI can become self-aware of its own energy consumption, fostering greener development.<\/p>\n

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

To drive these innovations, researchers are developing and utilizing a range of specialized tools and benchmarks:<\/p>\n