TY - GEN
T1 - Exploiting 2.5D/3D Heterogeneous Integration for AI Computing
AU - Wang, Zhenyu
AU - Sun, Jingbo
AU - Goksoy, Alper
AU - Mandal, Sumit K.
AU - Liu, Yaotian
AU - Seo, Jae Sun
AU - Chakrabarti, Chaitali
AU - Ogras, Umit Y.
AU - Chhabria, Vidya
AU - Zhang, Jeff
AU - Cao, Yu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - 2.5D
KW - 3D
KW - Chiplet
KW - Heterogeneous Integration
KW - ML accelerators
KW - Performance Analysis
UR - http://www.scopus.com/inward/record.url?scp=85189295237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85189295237&partnerID=8YFLogxK
U2 - 10.1109/ASP-DAC58780.2024.10473875
DO - 10.1109/ASP-DAC58780.2024.10473875
M3 - Conference contribution
AN - SCOPUS:85189295237
T3 - Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
SP - 758
EP - 764
BT - ASP-DAC 2024 - 29th Asia and South Pacific Design Automation Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 29th Asia and South Pacific Design Automation Conference, ASP-DAC 2024
Y2 - 22 January 2024 through 25 January 2024
ER -