Integrating Ethernet, PCIe, And UCIe For Enhanced Bandwidth And Scalability For AI/HPC Chips

Efficiently connecting the multiple CPUs and accelerators, various switches, and numerous NICs in modern data centers.

By Madhumita Sanyal, Synopsys
SemiEngineering (December 12th, 2024)

Multi-die architectures are becoming a pivotal solution for boosting performance, scalability, and adaptability in contemporary data centers. By breaking down traditional monolithic designs into smaller, either heterogeneous or homogeneous dies (also known as chiplets), engineers can fine-tune each component for specific functions, resulting in notable improvements in efficiency and capability. This modular strategy is especially advantageous for data centers, which demand high-performance, reliable, and scalable systems to process large volumes of data and complex AI workloads.

Hyperscale data centers, with their intricate and continually evolving architectures, can leverage various types of multi-die designs:

• Compute Dies: These are responsible for core processing tasks, including general-purpose CPUs, GPUs for parallel processing, and specialized accelerators for AI and machine learning.
• Memory Dies: These provide the essential storage and bandwidth for data-intensive applications, supporting various memory types such as DDR, HBM, and new non-volatile technologies.
• IO Dies: These manage input and output operations, ensuring efficient data transfer between compute dies and external interfaces like memory, networking, and storage, thus guaranteeing high data throughput and low latency.
• Custom Dies: These can be tailored to meet specific needs or optimize certain functions, including security dies for enhanced data protection, power management dies for efficient energy consumption, and networking dies for advanced communication capabilities.

This article explores how integrating multi-die designs with PCIe & Ethernet, in conjunction with UCIe IP, maximizes bandwidth and performance, facilitating the scaling up and out of modern AI data center infrastructures.

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