Matrix Operation Optimization Mechanism for Deep Learning

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Solution Overview

Problem

Deep learning applications face performance issues due to low compute utilization caused by resource-intensive data layout manipulation operations, such as transposing matrices, which consume excessive clock cycles and reduce graphics compute performance.

Innovation Solution

The implementation of instructions that define a two-dimensional memory surface to perform matrix layout manipulations before computations, allowing for efficient retrieval and manipulation of matrix data, thereby reducing the need for resource-intensive software-based operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If data layout manipulation operations (such as transposing matrices) are performed in software, then the data can be properly formatted for machine learning operations, but the compute performance is reduced due to excessive clock cycles consumed

Engineering Contradiction:

Improvedata layout manipulation capabilityVSAvoidcompute performance

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces software-based data layout manipulation with hardware-based operations in the graphics processing unit. Specifically, the GPU performs transpose and transform operations using dedicated hardware circuits, substituting the mechanical/software execution model with a hardware-based parallel processing model that operates at clock speed rather than software interpretation speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent performs data layout manipulation operations (transpose, transform) as preliminary actions before the main matrix multiplication computations. The GPU executes these layout operations in parallel with data loading and prepares the data in the required format ahead of time, so that when the compute kernels execute, the data is already in the optimal layout, eliminating the need for runtime manipulation.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If standard data layout format is used for machine learning operations, then framework compatibility is maintained, but resource-intensive transpose operations are required prior to execution

Engineering Contradiction:

Improveframework compatibilityVSAvoidresource consumption for transpose operations

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent implements a universal data layout manipulation mechanism within the GPU that can handle multiple operation types (transpose, transform, and other layout operations) through a single hardware infrastructure. The same hardware circuits that perform transpose operations also perform transform operations and other layout manipulations, providing multi-functionality that maintains framework compatibility while efficiently handling various data format requirements.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Adaptability or versatility

If matrix multiplication workloads with significant zeros (sparse matrices) are processed, then deep learning applications can be implemented, but compute utilization is low due to resources required for input data layout manipulation

Engineering Contradiction:

Improvedeep learning application supportVSAvoidcompute utilization

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent segments the data processing pipeline into distinct hardware-accelerated stages: data loading, layout manipulation (transpose/transform), and matrix multiplication. Each stage is handled by specialized hardware circuits within the GPU, allowing parallel execution and preventing any single stage from becoming a bottleneck. This segmentation enables efficient processing of sparse matrices by handling layout manipulation independently from the compute-intensive multiplication operations.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20240427842A1Matrix operation optimization mechanism

Publication Date: 2024.12.26 INTEL CORP

AI summary

An apparatus to facilitate machine learning matrix processing is disclosed. The apparatus comprises a memory to store matrix data one or more processors to execute an instruction to examine a message descriptor included in the instruction to determine a type of matrix layout manipulation operation that is to be executed, examine a message header included in the instruction having a plurality of parameters that define a two-dimensional (2D) memory surface that is to be retrieved, retrieve one or more blocks of the matrix data from the memory based on the plurality of parameters and a register file including a plurality of registers, wherein the one or more blocks of the matrix data is stored within a first set of the plurality of registers.