Tensor Layout and Type Conversion Using Single-Instruction Quantization

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Solution Overview

Problem

Existing computing environments face inefficiencies in processing tensor computations due to limitations in general-purpose processors and the need for hardware-specific programming, leading to increased complexity and resource usage.

Innovation Solution

A computer program product and method that reformats input tensors with different data-layout formats and data types using a single instruction, incorporating a transformation indicator and quantization parameters to optimize processing without software transformations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If many instructions are used on a general-purpose processor to perform tensor computations, then the computations can be performed using existing hardware, but the processing performance is limited and resource usage increases

Engineering Contradiction:

Improvecompatibility with existing hardwareVSAvoidprocessing performance

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent changes the data representation parameters by introducing tensor data structures with specific layout formats (row-major, column-major, compressed formats) and data type conversions. This allows the same hardware to efficiently process tensors in different formats without requiring multiple instruction sequences, thereby improving performance while maintaining hardware compatibility

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent creates a universal tensor processing instruction that can handle multiple operations (transformation, quantization, type conversion) within a single instruction cycle. This multi-functional instruction eliminates the need for separate software transformations for different tensor operations, improving processing performance while working with existing general-purpose processors

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If software transformations are used to convert between tensor formats and data types, then flexibility in processing different formats is achieved, but processing time increases

Engineering Contradiction:

Improveformat conversion flexibilityVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent performs tensor format transformations and data type conversions as preliminary actions during data loading or before computation begins, rather than as separate software transformations during execution. The single tensor processing instruction incorporates these transformations internally, completing them in hardware at optimized speeds before the main computation, thus reducing overall processing time while maintaining format flexibility

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces software-based transformation mechanisms with hardware-accelerated tensor processing instructions. By implementing transformation logic directly in the processor instruction set and executing it in hardware, the system eliminates the time-consuming software transformation step while preserving the ability to convert between different tensor formats and data types

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If purpose-built hardware implementation is used for tensor computations, then processing performance is improved, but programming complexity increases and verification costs increase

Engineering Contradiction:

Improveprocessing performanceVSAvoidprogramming complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements a universal tensor processing instruction that consolidates multiple specialized operations (format transformation, quantization, type conversion, computation) into a single hardware instruction. This allows purpose-built hardware to achieve high performance while maintaining programming simplicity, as developers can use a single instruction type rather than programming complex sequences for each operation

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent merges multiple separate processing steps (tensor format conversion, data type transformation, quantization, and computation) into a single integrated hardware instruction. This consolidation reduces programming complexity by eliminating the need to coordinate multiple separate operations, while the hardware implementation maintains high processing performance through optimized execution of the combined operation

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20260037789A1Tensor transformation

Publication Date: 2026.02.05 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Tensor transformation includes obtaining an input tensor having a first data-layout format and elements of a first data type, and reformatting the input tensor to provide an output tensor having a second data-layout format and elements of a second data type, where the second data-layout is different from the first data-layout format and the second data type is different from the first data type. Optionally, the reformatting includes element quantization on input elements of the input tensor, where the element quantization performed on an input element of the input tensor includes converting the input element to an output element, of the second data type, as an element of the output tensor, using the scale value to scale to the input element, the offset value to apply an offset, and the clip maximum and clip minimum values to enforce a maximum value and a minimum value for the output element.