Parallel Data Processing with Sparse Block Omission

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Solution Overview

Problem

Existing machine learning systems face significant data transfer times that slow down overall processing due to the transfer of large volumes of identical or nearly identical data, which can account for up to 70% of the training session time, and data compression introduces processing latency.

Innovation Solution

Implementing sparsity-aware communication protocols that transmit indicators of sparse data rather than the data itself, reducing the volume of data transferred and eliminating the need for compression, thereby optimizing data transmission and processing efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If data compression is applied to reduce data transfer volume, then data transfer efficiency is improved, but processing latency increases

Engineering Contradiction:

Improvedata transfer efficiencyVSAvoidprocessing latency

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent extracts and transmits only the non-sparse portions of data (first data) while omitting sparse data portions (second data) that match predetermined patterns. This selective extraction eliminates the need for compression algorithms, reducing processing latency while still achieving significant data transfer volume reduction.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The transmitting device performs preliminary identification of sparse data portions before transmission, marking them for omission. The receiving device uses indicators to reconstruct the complete dataset by combining received non-sparse data with locally stored or inferred sparse data, avoiding compression/decompression processing.

Inventive Principle:

Principle #10Preliminary action

2Loss of information

If large volumes of data are transmitted, then complete information is delivered, but data transfer time increases significantly

Engineering Contradiction:

Improvedata completenessVSAvoiddata transfer time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system extracts and transmits only essential non-sparse data portions while identifying and omitting redundant sparse data portions. Indicators are transmitted to mark omitted data locations, enabling the receiving device to reconstruct complete information by combining received data with locally stored sparse data, thereby reducing transfer time without losing information completeness.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The receiving device maintains local copies or representations of sparse data patterns and uses indicators from transmitted data to reconstruct the complete dataset. This copying approach allows the system to avoid transmitting redundant data while ensuring complete information recovery at the receiving end.

Inventive Principle:

Principle #26Copying

3Loss of information

If sparse data is transmitted in full, then no data is lost, but computing resources are wasted processing identical data

Engineering Contradiction:

Improvedata accuracyVSAvoidcomputing resources

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The system extracts and transmits only non-sparse data portions that contain meaningful information, while identifying and omitting sparse data portions that consist of identical or near-identical values. The receiving device uses indicators to reconstruct the complete dataset by filling in omitted sparse portions from local storage or inference, thereby preserving data accuracy while eliminating wasted computing resources on processing redundant identical data.

Inventive Principle:

Principle #2Taking out (Extraction)

4Quantity of substance

If data compression algorithms are used, then data transfer volume is reduced, but additional processing steps are introduced

Engineering Contradiction:

Improvedata transfer volumeVSAvoidprocessing complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

Instead of applying compression algorithms, the system extracts and transmits only non-sparse data portions while omitting sparse portions. Indicators mark the locations and characteristics of omitted data, enabling the receiving device to reconstruct the complete dataset through simple combination operations rather than complex decompression processing, thereby reducing both data transfer volume and processing complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20260004186A1Transmission and processing of data in parallel systems

Publication Date: 2026.01.01 XILINX INC

AI summary

Systems and methods for transmitting and processing data can use representations of data portions (e.g., blocks, chunks, or other subunits of data) that match a specified pattern, such as zero gradients in a machine learning training algorithm. These representations can allow different parts of a system to communicate the existence of these data portions to each other without actually transmitting the data portions while also allowing for the transmission of data portions that do not match the specified pattern. Processing of data can also use these representations or indicators as placeholders for the omitted data and perform calculations based on tallies, skipped memory locations, or other ways of accounting for the omitted data. This can in some cases reduce computing resources used to process data, such as data that may have been communicated using such representations.