Integrated Computing Architecture for Quantum Model Workloads

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Solution Overview

Problem

Current computing architectures, particularly those relying on Graphics Processing Units (GPUs), are not optimized for computation tasks involving differential equations, non-classical models, or tasks that require non-sequential and/or non-parallelizable operations, limiting their ability to handle diverse data sets effectively.

Innovation Solution

An integrated computing architecture that distributes layered data sets to different processing units based on layer type descriptors, utilizing a combination of GPUs for matrix operations and specialized processing units for tasks requiring non-classical models, such as quantum cognition models, to optimize computation efficiency and flexibility.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If all computation tasks are routed through GPUs, then parallel processing capability is maximized, but tasks requiring non-classical models or non-parallelizable operations cannot be handled effectively

Engineering Contradiction:

Improveparallel processing capabilityVSAvoidability to handle diverse computation tasks

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system segments computation tasks into different categories (parallelizable matrix operations vs. non-parallelizable/non-classical tasks) and routes them to appropriate processing units. The CPU identifies task types and separates workloads, sending matrix operations to GPUs while directing differential equations and quantum model tasks to specialized processing units.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The integrated computing architecture creates a universal system that can handle multiple types of computation tasks through different processing units. The CPU acts as a universal coordinator that can route various task types to appropriate specialized units (GPUs for parallel processing, specialized units for non-classical models), making the overall system adaptable to diverse computational needs.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If specialized processing units are added for non-classical models, then task diversity handling is improved, but system complexity increases

Engineering Contradiction:

Improveability to handle non-classical modelsVSAvoidnumber of processing units

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The CPU serves as an intermediary that manages the complexity of coordinating multiple processing units. It identifies task types, makes routing decisions, and orchestrates data flow between GPUs and specialized processing units, shielding users from the underlying system complexity while enabling diverse task handling.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system adds a new dimension to the computing architecture by introducing specialized processing units that operate in a different computational paradigm (non-classical models) alongside traditional GPUs. This dimensional expansion allows the system to handle previously intractable task types without fundamentally redesigning the entire architecture.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Device complexity

If GPUs handle all tasks, then hardware simplicity is maintained, but energy consumption increases for non-optimized tasks

Engineering Contradiction:

Improvehardware configurationVSAvoidenergy consumption

Core Design Contradiction:

Device complexityVSUse of energy by moving object

Solution Approach 1:

The system applies local quality optimization by matching specific task types to processing units with appropriate local characteristics. GPUs receive tasks optimized for their parallel architecture, while specialized processing units handle tasks requiring different computational properties, ensuring each unit operates in its energy-efficient zone.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system changes the operational parameters by introducing specialized processing units with different architectural parameters (optimized for non-classical models and non-parallelizable operations). This parameter diversification allows energy-efficient processing of previously GPU-inefficient task types.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250139477A1Integrated computing architecture for distributing layered data sets to processing units based on computation tasks including ones based on quantum models

Publication Date: 2025.05.01 IGNITE CHANNEL INC

AI summary

The present invention is directed to an integrated computing architecture designed for distributing a layered data set according to different computation tasks and to different processing units based on the layer type descriptors to carry out different computation tasks. Computation tasks involving matrix operations are distributed to Graphic Processing Units and others involving computation tasks requiring application of specialized computation models and distributed to one or more specialized processing units. The specialized processing units can include data layers that cannot be efficiently handled by GPUs and/or data layers that require models that include non-classical models such as quantum models. The integrated computing architecture is particularly useful when at least one of the data layers requires processing by a quantum cognition model and it may be extended by models with synthetic agents such as Artificial Intelligence Agents (AI Agents).