Thermal-Aware DSE Framework for 3DICs, With Advanced Cooling Models

A new technical paper titled “Cool-3D: An End-to-End Thermal-Aware Framework for Early-Phase Design Space Exploration of Microfluidic-Cooled 3DICs” was published by researchers at University of Michigan, Shanghai Jiao Tong University and University of Virginia.

Abstract
“The rapid advancement of three-dimensional integrated circuits (3DICs) has heightened the need for early-phase design space exploration (DSE) to minimize design iterations and unexpected challenges. Emphasizing the pre-register-transfer level (Pre-RTL) design phase is crucial for reducing trial-and-error costs. However, 3DIC design introduces additional complexities due to thermal constraints and an expanded design space resulting from vertical stacking and various cooling strategies. Despite this need, existing Pre-RTL DSE tools for 3DICs remain scarce, with available solutions often lacking comprehensive design options and full customization support. To bridge this gap, we present Cool-3D, an end-to-end, thermal-aware framework for 3DIC design that integrates mainstream architectural-level simulators, including gem5, McPAT, and HotSpot 7.0, with advanced cooling models. Cool-3D enables broad and fine-grained design space exploration, built-in microfluidic cooling support for thermal analysis, and an extension interface for non-parameterizable customization, allowing designers to model and optimize 3DIC architectures with greater flexibility and accuracy. To validate the Cool-3D framework, we conduct three case studies demonstrating its ability to model various hardware design options and accurately capture thermal behaviors. Cool-3D serves as a foundational framework that not only facilitates comprehensive 3DIC design space exploration but also enables future innovations in 3DIC architecture, cooling strategies, and optimization techniques. The entire framework, along with the experimental data, is in the process of being released on GitHub.”

Find the technical paper here. March 2025.

arXiv:2503.07297.
https://doi.org/10.48550/arXiv.2503.07297
Authors: Runxi Wang, Ziheng Wang, Ting Lin, Jacob M. Raby, Mircea R. Stan, Xinfei Guo.

This work was supported in part by the National Science Foundation of China and by the Semiconductor Research Corporation  (SRC) JUMP Center for Research on Intelligent Storage and Processing-in-memory (CRISP).