Programmable Lookup Table Conversion for Mixed-Precision AI Tasks

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Solution Overview

Problem

Existing approaches for converting between different numerical formats in AI-based tasks are computationally intensive, leading to increased power consumption, decreased hardware lifespan, and inefficient use of computational resources, particularly when processors are configured for higher precision datatypes than the actual workload requirements.

Innovation Solution

Employing programmable lookup tables (PLUTs) to efficiently convert AI-based tasks between different numerical formats, such as from lower to higher precision or vice versa, using a single instruction and extract operation, thereby reducing the need for extensive computations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Use of energy by moving object

If existing approaches are used for converting between numerical formats, then conversion capability is achieved, but computational intensity increases leading to higher power consumption

Engineering Contradiction:

Improvepower consumptionVSAvoidcomputational efficiency

Core Design Contradiction:

Use of energy by moving objectVSProductivity

Solution Approach 1:

The patent pre-computes and stores conversion lookup tables (PLUTs) that map between different numerical formats (e.g., FP8 to FP4, INT8 to INT4). During inference, instead of performing complex real-time conversions, the system simply queries the pre-computed PLUT, reducing computational intensity and power consumption while maintaining format conversion capability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a simplified copy of the conversion process using lookup tables that store pre-computed conversion results. This allows the system to replicate the conversion function without executing the heavy computational operations, thereby reducing power consumption while maintaining the conversion capability.

Inventive Principle:

Principle #26Copying

2Adaptability or versatility

If processors are configured for higher precision datatypes, then hardware capability is improved, but resource consumption increases when working with lower precision workloads

Engineering Contradiction:

Improvedatatype format compatibilityVSAvoidcomputational resources

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent dynamically adjusts the precision parameter by selecting appropriate lookup tables based on the workload requirements. When working with lower precision workloads (e.g., FP4 or INT4), the system uses corresponding PLUTs that enable efficient conversion to the processor's native precision format, allowing high-precision hardware to adapt to lower-precision workloads without wasting computational resources.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements a universal conversion framework where a single processor can handle multiple datatype formats (FP8, FP4, INT8, INT4) by selecting the appropriate PLUT. This multi-functional approach allows high-precision hardware to efficiently execute low-precision workloads through format conversion, improving adaptability without proportionally increasing resource consumption.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Adaptability or versatility

If comprehensive format conversion is implemented, then scalability is improved, but hardware complexity increases

Engineering Contradiction:

ImprovescalabilityVSAvoidhardware complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent extracts the complex conversion logic into separate, standalone lookup table structures (PLUTs) that are independent from the main processor architecture. This allows the conversion functionality to be implemented as simple table queries rather than integrated complex hardware circuits, thereby achieving scalability across multiple formats while keeping hardware complexity low.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent introduces lookup tables as intermediary structures between the workload datatype and the processor's native datatype. These PLUTs act as mediators that handle the format conversion task, allowing the processor to remain relatively simple while achieving broad format compatibility through the intermediary conversion tables.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250383871A1Processing for processors performing tasks using programmable lookup table

Publication Date: 2025.12.18 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Various embodiments described herein control circuitry of a computing device to cause the computing device to perform an AI-based task in a numerical format different from a numerical format in which the AI-based task is received. Embodiments of the technology described herein perform certain AI-based tasks based on a programmable lookup table (PLUT) that facilitates mapping the AI-based task from a first datatype format to a second datatype format matching the datatype format of the computing device assigned to perform the AI-based task. The conversion from datatypes is performed based on an instruction that includes performing a write operation and an extract operation using the PLUT. In this manner, certain computing devices employing the PLUT perform AI-based tasks quicker, with less power waste and more computational efficiency than using conventional technology, thereby improving hardware lifespan and efficiency on a clock cycle basis.