COFFEE: A Carbon-Modeling and Optimization Framework for HZO-based FeFET eNVMs

TL;DR

This paper introduces COFFEE, a comprehensive framework for modeling and optimizing the environmental impact of HZO-based FeFET eNVMs throughout their life cycle, including manufacturing and operation, to promote sustainable computing.

Contribution

It presents the first carbon modeling framework for HZO-based FeFET eNVMs, integrating embodied and operational carbon assessments with design space exploration tools.

Findings

HZO-FeFETs have up to 11% higher embodied carbon per unit area than CMOS baseline.

HZO-FeFETs offer about 4.3x lower embodied carbon per MB than SRAM.

Replacing SRAM with HZO-FeFETs in an edge ML accelerator reduces embodied carbon by 42.3% and operational carbon by 70%.

Abstract

Information and communication technologies account for a growing portion of global environmental impacts. While emerging technologies, such as emerging non-volatile memories (eNVM), offer a promising solution to energy efficient computing, their end-to-end footprint is not well understood. Understanding the environmental impact of hardware systems over their life cycle is the first step to realizing sustainable computing. This work conducts a detailed study of one example eNVM device: hafnium-zirconium-oxide (HZO)-based ferroelectric field-effect transistors (FeFETs). We present COFFEE, the first carbon modeling framework for HZO-based FeFET eNVMs across life cycle, from hardware manufacturing (embodied carbon) to use (operational carbon). COFFEE builds on data gathered from a real semiconductor fab and device fabrication recipes to estimate embodied carbon, and architecture level eNVM design space exploration tools to quantify use-phase performance and energy. Our evaluation shows that, at 2 MB capacity, the embodied carbon per unit area overhead of HZO-FeFETs can be up to 11% higher than the CMOS baseline, while the embodied carbon per MB remains consistently about 4.3x lower than SRAM across different memory capacity. A further case study applies COFFEE to an edge ML accelerator, showing that replacing the SRAM-based weight buffer with HZO-based FeFET eNVMs reduces embodied carbon by 42.3% and operational carbon by up to 70%.