Introduction
As artificial intelligence (AI) and machine learning (ML) technologies become increasingly integrated into our digital infrastructure, the demand for more powerful and efficient data centers continues to grow. This tutorial will explore the concept of Energy Efficient Interfaces (EEI) for AI/ML applications in optical networks, focusing on the work being done by the Optical Internetworking Forum (OIF) to address the challenges of power consumption and connectivity in next-generation data centers.
The Need for Energy Efficient Interfaces
The rapid adoption of AI and ML technologies has led to a significant increase in power consumption within data centers. Recent figures from the World Broadband Association indicate that the telecom industry's carbon footprint alone accounts for roughly 2% of global emissions. With the number of hyperscale data centers expected to grow threefold in the next six years, addressing energy efficiency has become a critical concern.
The above figure illustrates the architecture of an AI training cluster, highlighting the interconnectedness of GPU accelerators within compute structures. These accelerators serve as the workhorses behind data center servers, enabling advanced AI and ML capabilities.
However, the increasing need for GPU accelerator interconnectedness is growing faster than Moore's Law can keep up with, as shown in the following graph:
This graph demonstrates how the computational demands of AI and ML applications are outpacing the traditional improvements in processor technology, creating a pressing need for more efficient interconnect solutions.
The OIF Energy Efficient Interfaces (EEI) Framework
To address these challenges, the Optical Internetworking Forum (OIF) initiated the Energy Efficient Interfaces (EEI) Framework project in May 2023. This project aims to study new energy-efficient electrical and optical interfaces and identify opportunities for interoperability standards. The framework focuses on several key objectives:
Reduced power consumption
Improved density
Reduced latency
Ensuring link accountability
The EEI Framework encompasses various projects and working groups within OIF, including the Physical and Link Layer (PLL) Working Group and the EEI Physical Layer User Group (PLUG) System Vendor Requirements Project.
Types of Low Latency Links
The EEI Framework considers different categories of low latency links, depending on their type and what they interconnect:
Accelerator to I/O links
Accelerator to disaggregated memory links
Local cluster interconnects
Remote cluster interconnects
Each of these link types has specific requirements for physical and protocol layers, with a focus on minimizing latency. Factors contributing to latency include:
Forward Error Correction (FEC)
Digital signal processing in optical transceivers
Time of flight of photons in optical fibers (5 nanoseconds per meter, one-way)
Interface Solutions for Reducing Power Consumption
The industry has begun developing low-power pluggables for 100G Ethernet, known as Linear Pluggable Optics (LPO). These solutions eliminate the digital signal processor (DSP), significantly reducing power consumption in optical links. However, this approach can impact link accountability and limit adoption.
OIF is studying approaches to create robust, low-power, and low-latency solutions that are attractive in terms of:
Complexity
Cost
Integration with existing infrastructure
Reliable interoperability at higher data rates (e.g., 224G Ethernet and 128G PCIe7)
Energy-Efficient Link Configurations
OIF is exploring various energy-efficient link configurations that offer less than fully retimed interfaces. These configurations provide benefits such as:
Improved density for co-packaging applications
Reduced latency
Power reduction
Applications for these non-retimed links include:
Front panel pluggables
Near packaged modules
Co-packaged engines
Die-to-die electrical links
Several alternatives to legacy retimed links are being developed and tested:
Partially-retimed links
Non-retimed links for linearly amplified drive
Direct drive links
Host Tx predistortion links
Engine Tx predistortion links
When considering these alternatives, it's essential to evaluate the specific link type and application carefully. Collaboration with other standards organizations, such as PCI-SIG, CXL, UEC, and IEEE, is crucial for developing comprehensive solutions.
Challenges and Considerations
Implementing energy-efficient interfaces in optical networks presents several challenges:
Defining clear configurations for non-retimed links to create standards
Developing compliance methodologies to ensure interoperability between components
Balancing power reduction with link accountability
Addressing the increasing gap between compute demands and networking capabilities
The importance of adopting energy-efficient interfaces cannot be overstated. As Nathan Tracy of TE Connectivity and OIF president notes, "We are only at the leading edge of seeing the massive changes in deployment of these technologies, but these technologies come at a significant cost in terms of the power that next-generation data centers will consume and the resultant thermal power dissipation challenges. Something has to change to reduce power consumption trajectories."
Conclusion
The development of Energy Efficient Interfaces (EEI) for AI/ML applications in optical networks is crucial for the sustainable growth of data centers and the broader telecommunications industry. By addressing power consumption, density, latency, and link accountability, the OIF's EEI Framework aims to create interoperable solutions that can meet the demands of emerging technologies.
As Jeff Hutchins of Ranovus and OIF Physical and Link Layer Working Group Energy Efficient Interfaces Vice Chair emphasizes, "If we think about implementing an AI fabric into today's network, if we do nothing different and we just use today's methodologies it's just totally impractical from a power consumption and density standpoint. But if we make these changes, if we understand this challenge, then we can bring in these new architectures, which will benefit everyone."
By continuing to explore and develop energy-efficient interface solutions, the optical networking industry can ensure that the infrastructure supporting AI and ML applications remains sustainable and scalable for years to come.
Comments