Carbon Aware Transformers Through Joint Model-Hardware Optimization (2505.01386v2)

Abstract: The rapid growth of 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.

An Examination of Carbon Aware Transformers Through Joint Model-Hardware Optimization

The paper "Carbon Aware Transformers Through Joint Model-Hardware Optimization" introduces an innovative approach to reducing the carbon footprint of ML systems by emphasizing a comprehensive optimization strategy that includes both model and hardware considerations. The need for this research stems from the rapidly expanding utilization of ML models, which has not only heightened demand for computational resources but also intensified concerns about their environmental impact. The carbon footprint of ML systems encompasses operational carbon from training and inference activities, as well as embodied carbon from hardware manufacturing and lifecycle operations. This paper proposes a novel framework called "black" to address this complex issue.

Framework Introduction

The framework, termed "black," is designed to co-optimize ML models alongside hardware architectures within a carbon-aware context. This expands the traditional scope of optimizing solely for latency or energy efficiency by incorporating carbon metrics into the early stages of designing domain-specific hardware accelerators. The research demonstrates that optimizing for carbon can lead to design decisions that are markedly different from those made with only latency or energy efficiency in mind. The framework is applied to multi-modal models, particularly those akin to CLIP, resulting in a subsequent version named "CarbonCLIP." These models achieve up to a 17% reduction in total carbon emissions while maintaining competitive accuracy and latency compared to traditional baselines.

Theoretical and Practical Implications

The paper emphasizes the importance of comprehensive system designs that consider both embodied and operational carbon footprints. In particular, for computationally demanding models like multi-modal CLIP, optimizing across both hardware and software layers can substantially reduce environmental impacts without sacrificing performance. By co-optimizing model architectures with hardware, the framework offers a pathway to developing multi-modal systems that are both carbon-efficient and high-performing.

Numerical Results and Claims

The paper provides robust numerical evidence supporting the efficacy of their approach. Notably, the CarbonCLIP models exhibit considerable emissions reduction while sustaining performance metrics akin to state-of-the-art small CLIP models. This highlights the framework’s ability to reconcile environmental and performance objectives through meticulous design space exploration facilitated by multi-objective Bayesian optimization.

Future Directions in AI

The research raises critical considerations for the future development of AI systems—moving beyond performance metrics to prioritize sustainability. This shift may lead to the widespread adoption of similar frameworks, encouraging industry and academia to develop environmentally responsible AI technologies. Future advancements could extend the framework to other architectures and training contexts, including data-center environments, potentially leading to broader impact.

Conclusion

This paper provides a significant contribution to AI system design by integrating carbon metrics directly into the co-optimization process for ML models and hardware. This approach not only addresses the urgent need for reducing the environmental impact of AI technologies but also advances the field towards sustainable innovation. The application to CLIP-based systems serves as an exemplary case paper of how carbon-aware optimization can lead to substantial reductions in emissions while maintaining system efficacy—offering a scalable pathway for developing environmentally responsible, high-performance AI systems.