Tile-Based Processor Instructions for High-Throughput Matrix Processing

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Solution Overview

Problem

Existing processors face difficulties in efficiently handling large matrices due to instruction intensity and inefficient matrix operations, particularly in tasks like deep learning where low precision arithmetic is common.

Innovation Solution

Implementing matrix operations in computer hardware using 2-dimensional data structures called tiles, which are configured and operated on using specialized instructions and hardware support, including matrix (tile) multiplication, accumulation, and broadcast operations, with support for various data types and memory configurations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional processors are used for matrix operations, then general-purpose computing is maintained, but throughput and energy efficiency of matrix operations deteriorate

Engineering Contradiction:

Improvethroughput of matrix operationsVSAvoidenergy efficiency of matrix operations

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent divides large matrices into smaller tile structures that can be efficiently processed and manipulated. This segmentation allows the processor to handle matrix operations in manageable chunks, improving throughput by enabling parallel processing of multiple tiles while reducing energy consumption through more efficient data movement and computation.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces specialized tile-based data structures and dedicated matrix operation instructions that operate in a optimized computational dimension. By creating hardware and instruction set extensions specifically for matrix operations, the system achieves higher throughput and energy efficiency for deep learning workloads while maintaining general-purpose computing capabilities.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Power

If specialized hardware for matrix multiplication is implemented, then peak compute and energy efficiency are improved, but device complexity increases

Engineering Contradiction:

Improvepeak compute of matrix operationsVSAvoidcomplexity of processor architecture

Core Design Contradiction:

PowerVSDevice complexity

Solution Approach 1:

The patent implements matrix operation capabilities that can be integrated into existing processor architectures through instruction set extensions and compiler support. The tile-based approach and specialized instructions work alongside traditional scalar and vector operations, allowing the same hardware to handle both general-purpose computing and optimized matrix operations without requiring completely separate specialized hardware.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If low precision arithmetic is used in deep learning, then throughput is improved, but measurement precision of computations deteriorates

Engineering Contradiction:

Improvethroughput of deep learning computationsVSAvoidprecision of arithmetic operations

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent implements support for multiple precision formats including low-precision integer arithmetic (e.g., 8-bit, 16-bit) optimized for deep learning workloads. The tile-based matrix operations and specialized instructions are designed to efficiently handle low-precision computations while maintaining accuracy through careful design of accumulation and rounding operations, enabling high throughput without sacrificing necessary computational precision.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12455738B2Large-scale matrix restructuring and matrix-scalar operations

Publication Date: 2025.10.28 INTEL CORP

AI summary

Embodiments of apparatuses and methods for copying and operating on matrix elements are described. In embodiments, an apparatus includes a hardware instruction decoder to decode a single instruction and execution circuitry, coupled to hardware instruction decoder, to perform one or more operations corresponding to the single instruction. The single instruction has a first operand to reference a base address of a first representation of a source matrix and a second operand to reference a base address of second representation of a destination matrix. The one or more operations include copying elements of the source matrix to corresponding locations in the destination matrix and filling empty elements of the destination matrix with a single value.