Carbon-aware Design Space Exploration for AI Accelerators

Jiacong Sun , Georges Gielen and Marian Verhelst Hardware-efficient AI and ML
kuleuven-micas/carbonspot
  • Research Goals: Current AI hardware optimization primarily focuses on performance improvement while overlooking environmental impacts. The AI industry's computational demands are doubling approximately every 100 days, creating an urgent need for quantitative evaluation and optimization of Greenhouse Gas (GHG) emissions from both hardware and software perspectives. This project aims to evaluate the GHG emissions of AI accelerators and develop design methodologies to reduce their environmental impact.
  • Gap in the State of the Art: Current research on accelerator optimization predominantly focuses on performance metrics. While some GHG modeling studies exist, they primarily target commercial products or general CPU/GPU platforms. No existing work has thoroughly examined accelerator design or architectural implications when considering GHG emissions as an evaluation metric. This knowledge gap prevents accelerator designers from understanding the potential environmental impacts of their design decisions.
  • Results: This project introduces GHG evaluation into the existing performance-oriented modeling framework (ZigZag). Additionally, we developed an intuitive evaluation method to visualize hardware optimization benefits from both performance and GHG perspectives. Through multiple experiments conducted on this platform, our results demonstrate that carbon-optimal choices often differ from performance-optimal choices, highlighting the need for future co-optimization strategies that consider both metrics simultaneously.
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Jiacong Sun
Phd student
Georges Gielen
Academic staff
Marian Verhelst
Academic staff
CarbonSpot Framework Overview
CarbonSpot Framework Overview

Publications about this research topic

J. Sun, X. Yi, A. Symons, G. Gielen, L. Eeckhout and M. Verhelst, "An Analytical Model for Performance-Carbon Co-Optimization of Edge AI Accelerators," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, doi: 10.1109/TCAD.2025.3602746.

Yuhan Sun, Jiacong Sun, Xiaoling Yi, and Marian Verhelst. 2025. Is Chiplet the Key to Greener AI Accelerators? A Quantitative Benchmarking of Real Chiplet Architectures. SIGENERGY Energy Inform. Rev. 5, 2 (July 2025), 11–16.

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