Transformer Expert Reassignment for Dynamic Load Balancing

Solution Overview

Problem

Existing approaches to assigning experts in transformer models to processing units can negatively impact model accuracy, latency, and energy efficiency due to imbalanced processing loads caused by changing expert utilization over time.

Innovation Solution

A processing system that measures and dynamically adjusts the assignment of experts across processing units based on utilization metrics, transferring experts between nodes to balance the load and reduce variance in utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If experts are statically assigned to processing units, then device complexity is reduced, but processing load becomes imbalanced causing increased latency and energy consumption

Engineering Contradiction:

Improveexpert assignment complexityVSAvoidprocessing latency

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

The patent implements dynamic expert assignment where experts are reassigned to processing units based on real-time utilization metrics. The system continuously monitors expert utilization and redistributes experts from overloaded units to underutilized units, transforming the static assignment into a dynamic adaptation process that responds to changing workload conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system employs feedback mechanisms by measuring expert utilization metrics and using this information to guide expert reassignment decisions. The utilization measurements feed back into the assignment algorithm, creating a closed-loop control system that continuously optimizes the distribution of experts across processing units based on actual performance data.

Inventive Principle:

Principle #23Feedback

2Loss of time

If experts are dynamically reassigned based on utilization, then processing load is balanced improving latency, but device complexity increases

Engineering Contradiction:

Improveprocessing latencyVSAvoidexpert assignment complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The system implements dynamic expert assignment where experts are reassigned to processing units based on real-time utilization metrics. The system continuously monitors expert utilization and redistributes experts from overloaded units to underutilized units, transforming the static assignment into a dynamic adaptation process that responds to changing workload conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system employs feedback mechanisms by measuring expert utilization metrics and using this information to guide expert reassignment decisions. The utilization measurements feed back into the assignment algorithm, creating a closed-loop control system that continuously optimizes the distribution of experts across processing units based on actual performance data.

Inventive Principle:

Principle #23Feedback

3Use of energy by moving object

If experts are reassigned to balance load, then energy efficiency improves, but measurement and control complexity increases

Engineering Contradiction:

Improveenergy consumptionVSAvoidutilization measurement complexity

Core Design Contradiction:

Use of energy by moving objectVSDifficulty of detecting and measuring

Solution Approach 1:

The system employs feedback mechanisms by measuring expert utilization metrics and using this information to guide expert reassignment decisions. The utilization measurements feed back into the assignment algorithm, creating a closed-loop control system that continuously optimizes the distribution of experts across processing units based on actual performance data.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system implements self-service by having processing units automatically report their own utilization metrics and by enabling the assignment algorithm to autonomously make reassignment decisions based on these metrics. The system serves itself by using its own operational data to optimize its own resource distribution without requiring external intervention.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260003695A1Expert load balancing in transformer models

Publication Date: 2026.01.01 XILINX INC

AI summary

In response to one or more conditions, a processing system determines whether transferring one or more experts to different processing units would improve load balancing at the processing system. The processing system determines an amount of variance between the utilization for each expert relative to the average utilization of all experts at their currently-assigned processing units. The processing system then measures the amount of variance under one or more different configurations of expert-processing unit assignments. If so, the processing system transfers one or more of the experts to different processing units.