Pattern-Based Data Compression for Low-Latency Transfer
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Solution Overview
Problem
Existing data compression and decompression techniques require complex encoder and decoder designs, leading to increased latency, power consumption, and area in integrated circuit implementations, and often need prior information about the data type, which can limit their efficiency in transferring data blocks between processing units and memory systems.
Innovation Solution
The proposed solution involves data compression circuitry that detects predetermined patterns in data blocks and generates compressed data packets by representing only the non-patterned groups along with their positions, allowing for efficient compression and decompression without prior knowledge of the data type, using detector and generator circuitry to identify and reconstruct patterns, and optionally applying XOR or arithmetic combinations to increase pattern detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing data compression techniques are used, then data transfer efficiency is improved, but device complexity and implementation area increase
Solution Approach 1:
The data block is divided into multiple groups, where each group contains a subset of data values. The compression circuitry processes each group independently to detect patterns, allowing parallel processing and reducing overall complexity while maintaining compression efficiency.
Solution Approach 2:
The invention extracts only the essential information needed for compression - specifically, the group identifier and position indicator - rather than transmitting or processing all original data values. This extraction approach simplifies the encoder design while achieving effective compression.
2Productivity
If existing data compression techniques are used, then data transfer efficiency is improved, but latency and power consumption increase
Solution Approach 1:
By segmenting the data into groups and processing them independently with simple pattern detection, the compression operation completes faster than with complex sequential algorithms, reducing latency while maintaining efficiency.
Solution Approach 2:
The compression scheme uses self-descriptive group identifiers and position indicators that automatically convey structural information about the original data, eliminating the need for complex metadata processing and reducing overall processing time.
3Productivity
If existing data compression techniques are used, then data transfer efficiency is improved, but adaptability to different data types is reduced
Solution Approach 1:
The compression circuitry is designed to handle multiple data types and formats through a universal group-based processing approach. The same detection and compression logic applies regardless of whether the data represents integers, floating-point values, or other numeric types, enhancing versatility.
Solution Approach 2:
The invention allows flexible configuration of group size and data value subsets through parameters m and n, enabling adaptation to different data types and compression requirements without changing the fundamental compression mechanism.
Data Source
AI summary
Apparatus comprises data compression circuitry to process a set of data values, the data compression circuitry comprising: detector circuitry to detect, for each of n complementary groups of m data values of the set of data values, a first subset of the groups for which the data values in the group have a predetermined pattern of data values, where m and n are integers and m×n is the number of data values in the set of data values; generator circuitry to generate a compressed data packet comprising at least: a representation of a second subset of the groups, the second subset being each of then complementary groups other than groups in the first subset; and an indication of a group position, with respect to the set of data values, of each group in the second subset of groups. Complementary decompression apparatus is also described.