Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizations

• URL: http://arxiv.org/abs/2506.18289v1
• Date: Mon, 23 Jun 2025 04:52:08 GMT
• Title: Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizations
• Authors: Saurabhsingh Rajput, Mootez Saad, Tushar Sharma,
• Abstract summary: This paper emphasizes on treating energy efficiency as the first-class citizen and as a fundamental design consideration for a compute-intensive pipeline.<n>We show that strategic selection across five AI pipeline phases (data, model, training, system, inference) creates cascading efficiency.<n>Combinations reduce energy consumption by up to $94.6$% while preserving $95.95$% of the original F1 score of non-optimized pipelines.
• Score: 2.829284162137884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI's exponential growth intensifies computational demands and energy challenges. While practitioners employ various optimization techniques, that we refer as "knobs" in this paper, to tune model efficiency, these are typically afterthoughts and reactive ad-hoc changes applied in isolation without understanding their combinatorial effects on energy efficiency. This paper emphasizes on treating energy efficiency as the first-class citizen and as a fundamental design consideration for a compute-intensive pipeline. We show that strategic selection across five AI pipeline phases (data, model, training, system, inference) creates cascading efficiency. Experimental validation shows orthogonal combinations reduce energy consumption by up to $94.6$% while preserving $95.95$% of the original F1 score of non-optimized pipelines. This curated approach provides actionable frameworks for informed sustainable AI that balance efficiency, performance, and environmental responsibility.

Related papers

• Pathway-based Progressive Inference (PaPI) for Energy-Efficient Continual Learning [0.0]
  Continual learning systems face the dual challenge of preventing catastrophic forgetting while maintaining energy efficiency.<n>This paper introduces Pathway-based Progressive Inference (PaPI), a novel theoretical framework that addresses these challenges.<n>Our theoretical analysis shows that PaPI achieves an $mathcalO(K)$ improvement in the stability-plasticity trade-off.
  arXiv  Detail & Related papers  (2025-06-21T22:50:01Z)
• Energy Considerations of Large Language Model Inference and Efficiency Optimizations [28.55549828393871]
  As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise.<n>We systematically analyze the energy implications of common inference efficiency optimizations across diverse NLP and AI workloads.<n>Our findings reveal that the proper application of relevant inference efficiency optimizations can reduce total energy use by up to 73% from unoptimized baselines.
  arXiv  Detail & Related papers  (2025-04-24T15:45:05Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation [55.75188191403343]
  We introduce utility, which is a function predefined by each user and describes the trade-off between cost and performance of BO. We validate our algorithm on various LC datasets and found it outperform all the previous multi-fidelity BO and transfer-BO baselines we consider.
  arXiv  Detail & Related papers  (2024-05-28T07:38:39Z)
• Green AI in Action: Strategic Model Selection for Ensembles in Production [2.464194460689648]
  Ensemble learning, combining predictions from multiple models to form a single prediction, intensifies this problem due to cumulative energy consumption. This paper presents a novel approach to model selection that addresses the challenge of balancing the accuracy of AI models with their energy consumption in a live AI ensemble system.
  arXiv  Detail & Related papers  (2024-05-21T18:57:43Z)
• PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation [61.57833648734164]
  We propose a novel Parallel Yielding Re-Activation (PYRA) method for training-inference efficient task adaptation. PYRA outperforms all competing methods under both low compression rate and high compression rate.
  arXiv  Detail & Related papers  (2024-03-14T09:06:49Z)
• Interpretable Deep Reinforcement Learning for Optimizing Heterogeneous Energy Storage Systems [11.03157076666012]
  Energy storage systems (ESS) are pivotal component in the energy market, serving as both energy suppliers and consumers. To enhance ESS flexibility within the energy market, a heterogeneous photovoltaic-ESS (PV-ESS) is proposed. We develop a comprehensive cost function that takes into account degradation, capital, and operation/maintenance costs to reflect real-world scenarios.
  arXiv  Detail & Related papers  (2023-10-20T02:26:17Z)
• Model-based Causal Bayesian Optimization [74.78486244786083]
  We introduce the first algorithm for Causal Bayesian Optimization with Multiplicative Weights (CBO-MW) We derive regret bounds for CBO-MW that naturally depend on graph-related quantities. Our experiments include a realistic demonstration of how CBO-MW can be used to learn users' demand patterns in a shared mobility system.
  arXiv  Detail & Related papers  (2023-07-31T13:02:36Z)
• Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI [8.025202812165412]
  We consider energy consumption as a metric of equal importance to accuracy and to reduce any irrelevant tasks or energy usage. We examine the training stage of the deep learning pipeline from a sustainability perspective. We highlight innovative and promising energy-efficient practices for training deep learning models.
  arXiv  Detail & Related papers  (2023-03-24T12:48:21Z)
• HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression [69.36555801766762]
  We propose a hardware-aware tensor decomposition framework, dubbed HEAT, that enables efficient exploration of the exponential space of possible decompositions. We experimentally show that our hardware-aware factorized BERT variants reduce the energy-delay product by 5.7x with less than 1.1% accuracy loss.
  arXiv  Detail & Related papers  (2022-11-30T05:31:45Z)
• Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers [68.55472265775514]
  We consider an efficiency method as an operator applied on a model. In this paper, we study the plausibility of this idea, and the commutativity and cumulativeness of efficiency operators.
  arXiv  Detail & Related papers  (2022-07-31T18:01:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.