Evaluating Computing Platforms for Sustainability: A Comparative Analysis of FPGAs against ASICs, GPUs, and CPUs

TL;DR

This paper introduces GreenFPGA, a tool to estimate the total carbon footprint of FPGAs over their lifespan, comparing their sustainability to ASICs, GPUs, and CPUs across various deployment scenarios.

Contribution

The paper presents GreenFPGA, a comprehensive tool for modeling FPGA carbon footprint, considering multiple lifecycle stages and uncertainties, and analyzes sustainability regimes compared to other platforms.

Findings

FPGAs can be more sustainable than ASICs, GPUs, and CPUs under specific deployment regimes.

Application diversity and low-volume usage favor FPGA sustainability.

Analyzing different computing platforms is crucial for accurate CFP assessment.

Abstract

Climate change concerns emphasize the need for sustainable computing. Modeling the carbon footprint (CFP), including operational and embodied CFP from semiconductor use, manufacture and design, is essential. Field programmable gate arrays (FPGAs) stand out as promising platforms due to their reconfigurability across various applications, enabling the amortization of embodied CFP across multiple applications. This paper introduces GreenFPGA, a tool estimating the total CFP of FPGAs over their lifespan, considering uncertainties in CFP modeling. It accounts for CFP during design, manufacturing, reconfigurability (reuse), operation, disposal, testing, and recycling. GreenFPGA identifies deployment regimes in which FPGAs can be more sustainable than ASICs, GPUs, and CPUs under the modeled iso-performance assumptions. Experimental results highlight the importance of analyzing applications across different computing platforms to assess their CFP while varying parameters such as application type, lifetime, usage time, and volume impact their total CFP. Across the evaluated pairwise iso-performance case studies with ASICs, GPUs, and CPUs, FPGAs can be more sustainable under specific deployment regimes involving frequently changing, diverse workloads and low-volume applications.