RHODES: Robust Optimization for Uncertainty-Aware Design of CO2-Efficient Computing Systems

The inherent uncertainty in quantifying carbon footprint is a major challenge for designing environmentally sustainable computing systems. While existing efforts propose a variety of approaches to address this uncertainty, optimizing carbon footprint and energy efficiency together, while also accounting for uncertainty in carbon footprint, remains challenging. We identify two types of uncertainty in $\mathbf{C O}_{2}$-aware hardware design: data value $\left(\mathcal{D}_{\mathbf{V}}\right)$ uncertainty, where probability distributions of $\mathbf{C O}_{\mathbf{2}}$-related parameters exhibit high standard deviation (e.g., fluctuations in CO2 emissions of the power grid), and data availability $\left(\mathcal{D}_{\mathbf{A}}\right)$ uncertainty, in which probability distributions of $\mathbf{C O}_{\mathbf{2}}$-related parameters cannot even be reliably estimated due to a lack of data (e.g., carbon emissions during integrated circuit fabrication, with very few data points available). $\mathcal{D}_{A}$ uncertainty is especially challenging. To address this challenge, we leverage mathematical Robust Optimization (RO) techniques that enable uncertainty-aware decision-making without requiring explicit probability distributions. We present RHODES, a robust optimization framework for designing $\mathbf{C O}_{\mathbf{2}}$-efficient computing systems under carbon footprint data uncertainty. RHODES jointly models $\mathcal{D}_{\mathrm{v}}$ and $\mathcal{D}_{\mathrm{A}}$ uncertainties at multiple layers of the computing stack to optimize total carbon (tC) under uncertainty, and can target 3 different primary objectives (we include example results for specific systems, details in the full paper): (1) minimizing execution time given a performance constraint: designs that do not account for uncertainty incur 1.7× worse tC for the same performance constraint; (2) minimizing execution time given a tC constraint: RHODES produces robust designs with $\mathbf{1. 3 6} \times$ lower tC and only 0.2% execution time degradation, for complex heterogeneous system-on-chip (SoC), considering domain specific accelerators and workload level parallelism; and (3) minimizing total carbon delay product (tCDP, a metric of $\boldsymbol{C} O_{2}$ efficiency): improves tCDP by 1.3-3.17× vs. state-of-the-art $\mathbf{C O}_{2}$-aware optimization frameworks.