Exploiting 2.5D/3D Heterogeneous Integration for AI Computing

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

Abstract: The evolution of AI algorithms has not only revolutionized many application domains, but also posed tremendous challenges on the hardware platform. Advanced packaging technology today, such as 2.5D and 3D interconnection, provides a promising solution to meet the ever-increasing demands of bandwidth, data movement, and system scale in AI computing. This work presents HISIM, a modeling and benchmarking tool for chiplet-based heterogeneous integration. HISIM emphasizes the hierarchical interconnection that connects various chiplets through network-on-package. It further integrates technology roadmap, power/latency prediction, and thermal analysis together to support electro-thermal co-design. Leveraging HISIM with in-memory computing chiplets, we explore the advantages and limitations of 2.5D and 3D heterogenous integration on representative AI algorithms, such as DNNs, transformers, and graph neural networks.

External IDs:dblp:conf/aspdac/WangSGMLSCOC0024