Dual-Purpose Router Receptacles for Optics and AI Modules
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Solution Overview
Problem
The integration of high power optics modules and GPUs/DPUs into network devices is hindered by complex power, cooling, and control communications, leading to inefficiencies and resource consumption in data centers, particularly in AI/ML applications where traditional two-tier networks face congestion and latency issues.
Innovation Solution
An integrated router modular design with dual purpose receptacles that can interchangeably accommodate optical transceiver or AI processor modules, featuring remote cooling, separate power delivery, and direct attach switching fabric, reducing physical size, power consumption, and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional two-tier network architecture is used, then network coverage is provided, but network congestion and latency occur in AI/ML applications
Solution Approach 1:
The patent segments the traditional two-tier network architecture into distributed edge computing nodes that perform AI/ML processing locally. This segmentation eliminates the need for centralized data transmission through multiple network tiers, thereby reducing latency and improving throughput by processing data at the network edge rather than routing it through complex spine/leaf architectures.
Solution Approach 2:
The patent introduces a new dimensional approach by integrating AI processing capabilities directly into network edge devices, transitioning from a vertical two-tier hierarchy to a distributed horizontal architecture. This dimensional shift allows parallel processing across multiple edge nodes, eliminating network congestion and reducing latency inherent in traditional centralized architectures.
2Adaptability or versatility
If optical transceiver modules are installed in network devices, then connectivity is provided, but power consumption increases significantly
Solution Approach 1:
The patent merges optical transceiver functionality with AI processing capabilities into integrated edge computing nodes. By combining these functions in a single modular unit, the system achieves high connectivity while optimizing power consumption through shared resources and efficient resource allocation, eliminating the need for separate high-power optical modules in traditional network devices.
Solution Approach 2:
The patent creates universal edge computing nodes that can perform multiple functions including optical transceivers, AI processing, and network routing. This multi-functionality allows a single device to replace multiple specialized components, reducing overall power consumption while maintaining full connectivity capabilities across different network protocols and standards.
3Productivity
If GPUs and DPUs are installed to support AI/ML applications, then processing capability is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent implements dynamic resource allocation where edge computing nodes can dynamically switch between different processing modes and configurations based on workload requirements. This dynamic approach allows the system to optimize AI processing capability while managing complexity through software-defined resource orchestration, enabling flexible deployment of GPUs and DPUs only when and where needed rather than permanently installing complex hardware in all devices.
Solution Approach 2:
The patent introduces a virtualization layer that acts as an intermediary between AI processing resources and network operations. This mediator abstracts the complexity of GPU/DPU management, allowing high-level AI/ML applications to run on simplified interfaces while the intermediary handles the complex resource allocation, driver management, and hardware coordination, thereby improving processing capability without proportionally increasing system complexity.
4Adaptability or versatility
If high power optics modules are integrated into network devices, then transmission capability is enhanced, but cooling requirements and physical size increase
Solution Approach 1:
The patent employs a nested modular architecture where high-power optical transceiver modules are integrated within compact edge computing nodes. The nested design allows the optical modules to be efficiently packed within standardized form factors, with shared cooling infrastructure and power systems at higher hierarchical levels, thereby enhancing transmission capability without proportionally increasing overall device volume.
Solution Approach 2:
The patent applies local quality optimization by implementing targeted cooling solutions specifically at the optical module locations within edge nodes, rather than providing uniform cooling throughout the entire device. This localized approach efficiently manages heat from high-power optics while minimizing the overall cooling system size and device volume, maintaining high transmission capability with reduced physical footprint.
Data Source
AI summary
A device is provided that includes a chassis that contains at least one integrated circuit and a faceplate around a portion of the chassis. The faceplate having multiple face portions that expose a plurality of receptacles. At least one receptacle of the plurality of receptacles is a dual purpose receptacle configured to interchangeably receive an optical transceiver module or an artificial intelligence processor module and to enable connectivity with the at least one integrated circuit.