Neural Data Compression With Parallel Entropy Coding
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Solution Overview
Problem
Existing data compression techniques are inefficient due to prolonged processing times, leading to increased calculation resource costs and interference with other processing tasks.
Innovation Solution
A data compression system utilizing a parallel processing device that divides data into partial units, applies neural networks for probability estimation, and performs entropy coding to improve processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If neural network-based compression processing is used, then data volume is reduced, but processing time increases
Solution Approach 1:
The patent divides compression target data into multiple pieces of partial data and processes them in parallel using multiple processing units. This segmentation allows the system to maintain high compression ratios through neural network processing while reducing overall processing time by executing multiple operations simultaneously rather than sequentially.
Solution Approach 2:
The patent transitions from sequential single-threaded processing to parallel multi-threaded processing, adding a dimensional aspect of concurrency. By utilizing multiple processing units that can operate independently on different data segments, the system achieves speedup without compromising compression effectiveness.
2Quantity of substance
If compression processing is performed, then data volume is reduced, but calculation resource costs increase
Solution Approach 1:
By segmenting data into partial pieces and processing them in parallel, the system distributes calculation workload across multiple units. This approach reduces the time each unit needs to operate, thereby reducing total energy consumption despite using multiple processing units, as parallel execution completes the task faster than sequential processing would take.
Solution Approach 2:
The patent maintains continuous useful action by keeping multiple processing units actively engaged in compression operations simultaneously. Rather than having idle periods between sequential operations, all processing units contribute to compression work throughout the execution time, maximizing resource utilization efficiency and reducing overall calculation resource costs.
Data Source
AI summary
A computer system for compressing data includes a parallel processing device. The data parallel processing device is configured to divide compression target data into a plurality of pieces of partial data, execute compression processing on the partial data in parallel, calculate an appearance probability for each predetermined data unit of the partial data by using a neural network in the compression processing, and output a coded bit string which is a bit string subjected to entropy coding to the data unit based on the data unit and the appearance probability. Processing of implementing the neural network includes first conversion processing of executing matrix multiplication processing, and second conversion processing of inputting a processing result of the first conversion processing and converting each element of a matrix resulting from the processing result into an integer of 1 bit, subsequent to the first conversion processing.