Asynchronous Gradient Weight Compression via Pointer Tracking

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Solution Overview

Problem

Distributed machine learning systems face challenges in efficiently updating weights across learners due to communication bottlenecks and staleness issues in asynchronous gradient descent, leading to compromised model accuracy and inefficiencies.

Innovation Solution

The implementation of a gradient weight compression system that identifies and computes a second concatenated compressed gradient weight based on the differences in the first concatenated compressed gradient weight, using a pointer component to track timestamps and update weights in a machine learning system, facilitating efficient communication and reducing computational costs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If asynchronous gradient descent is used to enable parallel weight updates across learners, then processing speed and productivity are improved, but communication bottlenecks and staleness issues arise that compromise model accuracy

Engineering Contradiction:

Improveweight update speedVSAvoidmodel accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The gradient weight update process is segmented into independent compressed gradient computations at each learner node, allowing parallel processing without full synchronization. Each learner compresses and transmits gradients independently, eliminating the communication bottleneck while maintaining model consistency through selective gradient application.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Gradients are compressed before transmission to reduce communication overhead and prepare data for efficient aggregation. The compression occurs preliminarily at each learner node, enabling faster transmission and reducing the time learners need to wait for gradient synchronization, thereby improving productivity while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If full gradient transmission is used to maintain model parameter consistency, then model accuracy is improved, but communication overhead and computational costs increase

Engineering Contradiction:

Improvemodel parameter consistencyVSAvoidcomputational power consumption

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

Only the essential gradient information needed for model updates is extracted and transmitted, rather than transmitting complete gradient data. The compression process extracts key weight update signals while discarding redundant information, reducing communication overhead and computational energy consumption while preserving model parameter consistency.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The gradient data undergoes parameter transformation through compression techniques that represent full gradient information using fewer parameters. This changes the representation from high-dimensional complete gradients to compressed forms that require less computational power to transmit and process, while maintaining the essential update information.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If frequent gradient synchronization is performed to maintain model consistency, then model accuracy is improved, but communication overhead and processing time increase

Engineering Contradiction:

Improvemodel parameter consistencyVSAvoidsynchronization time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system uses disposable compressed gradient representations that are transmitted and applied without requiring expensive, time-consuming full synchronization. Each compressed gradient is a lightweight, short-lived data structure that enables rapid updates without the overhead of traditional synchronization protocols, reducing both time and computational costs.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

Data Source

PatentUS11816549B2Asynchronous gradient weight compression

Publication Date: 2023.11.14 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Systems, computer-implemented methods, and computer program products to facilitate gradient weight compression are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a pointer component that can identify one or more compressed gradient weights not present in a first concatenated compressed gradient weight. The computer executable components can further comprise a compression component that can compute a second concatenated compressed gradient weight based on the one or more compressed gradient weights to update a weight of a learning entity of a machine learning system.