Prefetching Random Data for Machine Learning Cache Efficiency

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Solution Overview

Problem

Current computer systems lack instructions for prefetching random data into cache lines and using random data implicitly, which is essential for tasks like training machine learning algorithms, requiring extra time and energy to generate and store random values.

Innovation Solution

Introduce the PREFETCHRDM instruction to prefetch random data into cache lines and the {OP}WRND instruction to use random data implicitly, allowing random data to be directly utilized in operations without explicit location specification, optimizing the process for machine learning tasks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional random number generation methods are used, then random data can be generated, but extra time and energy are required to generate and store random values

Engineering Contradiction:
Improverandom data generation efficiencyVSAvoidtime for generating and storing random values
Core Design Contradiction:
ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary action by prefetching random data into cache lines before it is actually needed for machine learning operations. The PREFETCHRDM instruction triggers random data generation and caching in advance, so that when the random data is needed, it is already available in the cache, eliminating the time penalty of on-demand generation.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The system implements self-service by having the cache automatically generate and store random data using the PREFETCHRDM instruction. The random number generator is integrated into the cache structure, allowing it to service its own random data needs without external intervention, thereby reducing the overall time and energy required for random data acquisition.

Inventive Principle:
Principle #25Self-service

2Ease of operation

If explicit random value generation and storage instructions are used, then random data can be obtained, but the process requires multiple separate operations increasing complexity

Engineering Contradiction:
Improverandom data utilization simplicityVSAvoidnumber of instructions for random data operations
Core Design Contradiction:
Ease of operationVSDevice complexity

Solution Approach 1:

The invention merges the random data generation, caching, and retrieval operations into a single integrated mechanism. The PREFETCHRDM instruction combines prefetching with random data generation, while the {OP}WRND instruction combines random data retrieval with implicit usage in operations, reducing the number of separate instructions needed and simplifying the operational workflow.

Inventive Principle:
Principle #5Merging (Combining)

Solution Approach 2:

The cache structure is given multi-functionality by enabling it to both store regular data and generate/store random data. The same cache lines can be used for deterministic data or randomized data depending on the instruction executed, eliminating the need for separate hardware structures for different data types and reducing overall system complexity.

Inventive Principle:
Principle #6Universality (Multi-functionality)

3Use of energy by moving object

If random data is not prefetched into cache, then cache memory is saved, but time and energy are wasted on generating and storing random values when needed

Engineering Contradiction:
Improveenergy for random data operationsVSAvoidtime for random value generation and storage
Core Design Contradiction:
Use of energy by moving objectVSLoss of time

Solution Approach 1:

The system performs preliminary action by prefetching random data into cache lines before it is actually needed for machine learning operations. The PREFETCHRDM instruction triggers random data generation and caching in advance, so that when the random data is needed, it is already available in the cache, eliminating the time penalty of on-demand generation.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The system implements dynamics by making the cache usage flexible - cache lines can be used for deterministic data or randomized data depending on the instruction executed. This dynamic allocation allows the system to optimize for either speed (by using cache for random data) or memory efficiency (by using cache for regular data) based on the specific operational context.

Inventive Principle:
Principle #15Dynamics

Data Source

PatentEP4202656B1Random data usage
Publication Date: 2024.11.06 INTEL CORP
  • EP4202656B1 patent drawingFigure 1
  • EP4202656B1 patent drawingFigure 2
  • EP4202656B1 patent drawingFigure 3

AI summary

Techniques for prefetching random data and instructions using implicitly reference random number data are described. An example includes decode circuitry to decode a single instruction at least having a field for an opcode, the opcode to indicate execution circuitry is to perform an operation using implicitly referenced random data; and execution circuitry to execute the decoded single instruction according to the opcode.