Waveform Data Compression Across Multi-Core Processors

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Solution Overview

Problem

Existing waveform data compression techniques often fail to efficiently compress both integer and floating-point data types simultaneously, limiting their applicability in multi-core processing systems where both data types are commonly used, and they typically offer only one compression mode (lossless or lossy) without the ability to adapt to real-time data transfer requirements.

Innovation Solution

A configurable compression and decompression system that can handle both integer and floating-point data formats, supporting lossless and lossy modes, and adapting to produce fixed bit rate or quality metrics, enabling efficient data transfer between processor cores and memory in multi-core processing environments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing waveform data compression techniques are used, then compression of single data type is achieved, but inability to compress both integer and floating-point data types simultaneously limits applicability

Engineering Contradiction:

Improvecompression mode adaptabilityVSAvoiddata type compatibility

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The compression system dynamically adapts its operation mode based on the data type being processed. The system can switch between lossless and lossy compression modes, and between different data type handling (integer vs floating-point), allowing it to optimize performance for each specific data type while maintaining broad compatibility across multiple data types simultaneously

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The compression system is designed to handle multiple data types (integer and floating-point) and multiple compression modes (lossless and lossy) within a single unified architecture. This multi-functional design eliminates the need for separate compression systems for different data types, thereby improving versatility without sacrificing reliability through specialized handling of each data type's unique characteristics

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Quantity of substance

If compression is applied to waveform data, then bandwidth requirements are reduced, but latency is introduced due to compression and decompression operations

Engineering Contradiction:

Improvedata transfer volumeVSAvoiddata transfer latency

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The system changes compression parameters dynamically based on the specific waveform data characteristics, data type, and transfer requirements. By adjusting compression intensity and mode (lossless vs lossy), the system optimizes the balance between compression ratio and processing speed, reducing latency while still achieving significant bandwidth reduction for large data volumes

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system applies partial compression selectively to portions of the waveform data that benefit most from compression, rather than uniformly compressing all data. This approach minimizes the overhead of compression operations on data that would not benefit significantly, thereby reducing overall latency while still achieving substantial bandwidth reduction for the compressed portions

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If lossy compression mode is used, then compression ratio is improved, but data quality degradation occurs

Engineering Contradiction:

Improvecompression efficiencyVSAvoiddata accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system dynamically adjusts compression parameters including the degree of lossiness, precision levels, and compression intensity based on the specific waveform data characteristics and application requirements. This allows the system to achieve high compression ratios when data quality requirements are flexible, while maintaining data accuracy when precision is critical, thereby optimizing the trade-off between productivity and measurement precision

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS9319063B2Enhanced multi-processor waveform data exchange using compression and decompression

Publication Date: 2016.04.19 ALTERA CORP

AI summary

Configurable compression and decompression of waveform data in a multi-core processing environment improves the efficiency of data transfer between cores and conserves data storage resources. In waveform data processing systems, input, intermediate, and output waveform data are often exchanged between cores and between cores and off-chip memory. At each core, a single configurable compressor and a single configurable decompressor can be configured to compress and to decompress integer or floating-point waveform data. At the memory controller, a configurable compressor compresses integer or floating-point waveform data for transfer to off-chip memory in compressed packets and a configurable decompressor decompresses compressed packets received from the off-chip memory. Compression reduces the memory or storage required to retain waveform data in a semiconductor or magnetic memory. Compression reduces both the latency and the bandwidth required to exchange waveform data. This abstract does not limit the scope of the invention as described in the claims.