Dynamic AI Accelerator Topology via Flexible Cable Interconnects

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Solution Overview

Problem

Existing AI accelerator chip topologies are either clumsy or require substantial hardware overhead, making them inefficient for distributed AI training, which necessitates the development of a more dynamic and flexible connectivity solution.

Innovation Solution

The implementation of a data processing system with dynamically activatable and deactivatable inter-card and inter-chip connections using cable connections, such as CCIX, allows for the creation of AI chip topologies of varying sizes by activating or deactivating connections between base boards, enabling efficient AI model training with reduced hardware overhead.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If PCB wires on base board are used to connect AI accelerator chips, then small topology can be built, but the approach becomes clumsy and requires substantial hardware overhead

Engineering Contradiction:
Improvetopology size flexibilityVSAvoidhardware overhead
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamically reconfigurable interconnects that can be activated or deactivated based on training requirements. Cable connections between base boards can be dynamically enabled or disabled to create topologies of varying sizes, allowing the system to adapt from small to large configurations without permanent hardwired connections for all possible configurations.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The system divides the AI accelerator cluster into multiple base boards, each containing multiple chips. These base boards can be independently connected or disconnected via cable interconnects, allowing modular assembly of different topology sizes. Each base board segment can function semi-independently and be combined in various configurations.

Inventive Principle:
Principle #1Segmentation

2Adaptability or versatility

If Ethernet is used to connect different base boards for large topology, then scalability is improved, but hardware overhead and complexity increase substantially

Engineering Contradiction:
Improvetopology scalabilityVSAvoidhardware overhead
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The cable interconnects serve multiple functions: they provide high-speed communication between base boards for large topology configurations, can be individually activated or deactivated for dynamic reconfiguration, and replace the need for extensive PCB wiring infrastructure. The same physical infrastructure supports both small and large topologies through selective activation.

Inventive Principle:
Principle #6Universality (Multi-functionality)

Solution Approach 2:

Instead of creating unique hardwired connections for each possible topology configuration, the system uses reusable cable connections that can be plugged and unplugged to create different topologies. The same set of cables can be configured multiple times for different training needs without requiring dedicated wiring for each configuration.

Inventive Principle:
Principle #26Copying

3Device complexity

If fixed topology configurations are used, then hardware overhead is reduced, but adaptability to different training needs is limited

Engineering Contradiction:
Improvehardware overheadVSAvoidtraining configuration flexibility
Core Design Contradiction:
Device complexityVSAdaptability or versatility

Solution Approach 1:

The system employs dynamically controllable interconnects that can be activated or deactivated through control logic. This allows the hardware overhead to remain minimal (cables connected but not necessarily active) while providing full adaptability to switch between different topology configurations based on training requirements.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The system changes the operational state of interconnects (activated/deactivated) rather than physically reconfiguring the hardware structure. By changing the activation parameter of existing cable connections, the system achieves different topology configurations without adding or removing physical hardware, maintaining low overhead while providing high adaptability.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentEP3841530B1Distributed ai training topology based on flexible cable connection
Publication Date: 2023.11.08 KUNLUNXIN TECHNOLOGY (BEIJING) CO LTD
  • EP3841530B1 patent drawingFigure 1
  • EP3841530B1 patent drawingFigure 2A~2C
  • EP3841530B1 patent drawingFigure 2D~2F

AI summary

A data processing system includes a central processing unit (CPU) (107,109) and ac-celerator cards coupled to the CPU (107,109) over a bus, each of the accelerator card-s having a plurality of data processing (DP) accelerators to receive DP tasks from the CPU (107,109) and to perform the received DP tasks. At least two of the accelerator cards are coupled to each other via an inter-card connection, and at least two of the DP accelerators are coupled to each other via an inter-chip connection. Each of the inter-card connection and the inter-chip connection is capable of being dynamically activated or deactivated, such that in response to a request received from the CPU (107,109), any one of the accelerator cards or any one of the DP accelerators within any one of the accelerator cards can be enabled or disabled to process any one of the DP tasks received from the CPU (107,109).