Carbon Aware Transformers Through Joint Model-Hardware Optimization

• URL: http://arxiv.org/abs/2505.01386v2
• Date: Thu, 08 May 2025 22:21:11 GMT
• Title: Carbon Aware Transformers Through Joint Model-Hardware Optimization
• Authors: Irene Wang, Newsha Ardalani, Mostafa Elhoushi, Daniel Jiang, Samuel Hsia, Ekin Sumbul, Divya Mahajan, Carole-Jean Wu, Bilge Acun,
• Abstract summary: There is a lack of tools to holistically quantify and optimize the total carbon footprint of machine learning systems.<n>We propose CATransformers, a carbon-aware architecture search framework that enables sustainability-driven co-optimization of ML models and hardware architectures.<n>We apply our framework to multi-modal CLIP-based models, producing CarbonCLIP, a family of CLIP models achieving up to 17% reduction in total carbon emissions.
• Score: 6.731982787210602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of machine learning (ML) systems necessitates a more comprehensive evaluation of their environmental impact, particularly their carbon footprint, which comprises operational carbon from training and inference execution and embodied carbon from hardware manufacturing and its entire life-cycle. Despite the increasing importance of embodied emissions, there is a lack of tools and frameworks to holistically quantify and optimize the total carbon footprint of ML systems. To address this, we propose CATransformers, a carbon-aware architecture search framework that enables sustainability-driven co-optimization of ML models and hardware architectures. By incorporating both operational and embodied carbon metrics into early design space exploration of domain-specific hardware accelerators, CATransformers demonstrates that optimizing for carbon yields design choices distinct from those optimized solely for latency or energy efficiency. We apply our framework to multi-modal CLIP-based models, producing CarbonCLIP, a family of CLIP models achieving up to 17% reduction in total carbon emissions while maintaining accuracy and latency compared to state-of-the-art edge small CLIP baselines. This work underscores the need for holistic optimization methods to design high-performance, environmentally sustainable AI systems.

Related papers

• Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization [48.70916202664808]
  DiffCarl is a diffusion-modeled carbon- and risk-aware reinforcement learning algorithm for intelligent operation of multi-microgrid systems.<n>It outperforms classic algorithms and state-of-the-art DRL solutions, with 2.3-30.1% lower operational cost.<n>It also achieves 28.7% lower carbon emissions than those of its carbon-unaware variant and reduces performance variability.
  arXiv  Detail & Related papers  (2025-07-22T03:27:07Z)
• Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainability [7.059399419316343]
  3D integration improves performance but introduces sustainability challenges.<n>We propose a carbon-efficient design methodology for 3D accelerators.<n>Our approach effectively reduces silicon area and fabrication overhead while maintaining high computational accuracy.
  arXiv  Detail & Related papers  (2025-04-14T03:48:37Z)
• AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services [14.664814078159282]
  Large language models (LLMs) have become a growing concern due to their substantial energy consumption and carbon footprint.<n>We propose AOLO, a framework for analysis and optimization for low-carbon oriented wireless LLM services.<n>AOLO introduces a comprehensive carbon footprint model that quantifies greenhouse gas emissions across the entire LLM service chain.<n>We propose a low-carbon-oriented optimization algorithm, i.e., SNN-based deep reinforcement learning (SDRL)
  arXiv  Detail & Related papers  (2025-03-06T13:21:38Z)
• 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)
• Carbon Market Simulation with Adaptive Mechanism Design [55.25103894620696]
  A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility. We propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL) Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions.
  arXiv  Detail & Related papers  (2024-06-12T05:08:51Z)
• Beyond Efficiency: Scaling AI Sustainably [4.711003829305544]
  Modern AI applications have driven ever-increasing demands in computing. This paper characterizes the carbon impact of AI, including both operational carbon emissions from training and inference as well as embodied carbon emissions from hardware manufacturing.
  arXiv  Detail & Related papers  (2024-06-08T00:07:16Z)
• Carbon-aware Software Services [3.105112058253643]
  This article proposes a novel framework for implementing, configuring and assessing carbon-aware interactive software services. We propose a methodology to implement carbon-aware services leveraging the Strategy design pattern to feature alternative service versions with different energy consumption. We devise a bilevel optimisation scheme to configure which version to use at different times of the day, based on forecasts of carbon intensity and service requests.
  arXiv  Detail & Related papers  (2024-05-21T08:26:38Z)
• Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
  Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT) In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
  arXiv  Detail & Related papers  (2024-04-28T05:46:28Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators [58.728312684306545]
  Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale.
  arXiv  Detail & Related papers  (2022-10-31T04:04:03Z)
• Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
  We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions. We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
  arXiv  Detail & Related papers  (2022-06-10T17:04:04Z)

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.