Chiplets For Generative AI Workloads: Challenges in both HW and SW


A new technical paper titled “Challenges and Opportunities to Enable Large-Scale Computing via Heterogeneous Chiplets” was published by researchers at University of Pittsburgh, Lightelligence, and Meta.

“Fast-evolving artificial intelligence (AI) algorithms such as large language models have been driving the ever-increasing computing demands in today’s data centers. Heterogeneous computing with domain-specific architectures (DSAs) brings many opportunities when scaling up and scaling out the computing system. In particular, heterogeneous chiplet architecture is favored to keep scaling up and scaling out the system as well as to reduce the design complexity and the cost stemming from the traditional monolithic chip design. However, how to interconnect computing resources and orchestrate heterogeneous chiplets is the key to success. In this paper, we first discuss the diversity and evolving demands of different AI workloads. We discuss how chiplet brings better cost efficiency and shorter time to market. Then we discuss the challenges in establishing chiplet interface standards, packaging, and security issues. We further discuss the software programming challenges in chiplet systems.”

Find the technical paper here. Published October 2023.

Zhuoping Yang, Shixin Ji, Xingzhen Chen, Jinming Zhuang, Weifeng Zhang, Dharmesh Jani, Peipei Zhou. arXiv:2311.16417v1

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