HISIM: Analytical Performance Modeling and Design Space Exploration of 2.5D/3D Integration for AI Computing

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Zhenyu Wang, Pragnya Sudershan Nalla, Jingbo Sun, A. Alper Goksoy, Sumit K. Mandal, Jae-Sun Seo, Vidya A. Chhabria, Jeff Zhang, Chaitali Chakrabarti, Ümit Y. Ogras, Yu Cao

Published: 01 Jan 2025, Last Modified: 07 Nov 2025IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Monolithic designs face significant fabrication cost and data movement challenges, especially when executing complex and diverse AI models. Advanced 2.5D/3D packaging promises high bandwidth and connection density to overcome these challenges, yet it also introduces new electro-thermal constraints. This article develops a suite of analytical performance models to enable efficient benchmarking of a 2.5D/3D heterogeneous system for energy-efficient AI computing. These models encompass various performance metrics related to computing units, network-on-chip (NoC), and network-on-package (NoP). The results are summarized into a new tool, HISIM, which is $10^{4} \times $ – $10^{6} \times $ faster than state-of-the-art AI benchmark tools. Furthermore, HISIM integrates rapid thermal simulation for the 2.5D/3D system, helping shed light on both the potential and limitations of 2.5D/3D heterogeneous integration (HI) on representative AI algorithms. The code of HISIM is available at https://github.com/mec-UMN/HISIM.