Information Processing With Define-by-Run Graph Generation

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Solution Overview

Problem

Existing frameworks for creating neural network source codes are inefficient and struggle with complex model implementation, memory management, and dynamic graph changes, leading to high development and maintenance costs and difficulty in debugging.

Innovation Solution

A new calculation procedure called 'Define-by-Run' dynamically extracts and stores graph structures during learning, allowing meta changes and recalculating gradients, enabling the use of general-purpose programming languages for more complex graph structures and improving memory efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If a predetermined framework is used to create neural network source code, then the creation process is simplified, but the framework becomes inefficient and struggles with complex model implementation

Engineering Contradiction:

Improveease of creating source codeVSAvoidability to handle complex model implementation

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic graph construction by executing Forward processing code to automatically generate the computational graph structure during runtime, rather than requiring static framework definitions. This allows the system to adapt to complex model implementations dynamically while maintaining ease of use through automatic graph generation from simple Forward processing code.

Inventive Principle:

Principle #15Dynamics

2Device complexity

If static graph structures are used, then memory management is simplified, but dynamic graph changes become difficult and debugging is challenging

Engineering Contradiction:

Improvesimplicity of memory managementVSAvoidability to perform dynamic graph changes

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system executes Forward processing code to dynamically construct the computational graph during runtime, enabling automatic adaptation to graph structure changes without requiring manual memory management adjustments. The executing unit automatically manages memory allocation and deallocation based on the dynamically generated graph structure, maintaining simplicity while enabling flexibility.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The Forward processing code automatically generates the computational graph structure and associated memory management information without requiring manual intervention. The system self-services by extracting graph structures from the execution of Forward processing code, automatically handling memory allocation and deallocation based on the generated graph, thereby simplifying memory management while enabling dynamic changes.

Inventive Principle:

Principle #25Self-service

3Productivity

If existing frameworks are used, then development is faster, but development and maintenance costs increase due to inefficiency

Engineering Contradiction:

Improvespeed of developmentVSAvoiddevelopment and maintenance costs

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system automatically generates computational graph structures by executing Forward processing code, eliminating the need for manual graph construction and reducing development time. The automatic extraction of graph structures from Forward processing execution streamlines the development process while reducing maintenance costs through automated graph generation and updates, improving productivity without increasing costs.

Inventive Principle:

Principle #25Self-service

4Manufacturing precision

If manual graph construction is performed, then control over graph structure is precise, but the process becomes time-consuming and inefficient

Engineering Contradiction:

Improveprecision of graph structure controlVSAvoidtime required for graph construction

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The Forward processing code automatically generates the computational graph structure through its execution, eliminating manual graph construction while maintaining precise control over the graph structure. The executing unit extracts the graph structure directly from the Forward processing code execution, ensuring accuracy while significantly reducing the time required compared to manual construction methods.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250328768A1Information processing device and information processing method

Publication Date: 2025.10.23 PREFERRED NETWORKS INC

AI summary

There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.