Compressed Data Stream Positioning for Random Weight Access
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Solution Overview
Problem
Accessing specific data within a compressed data stream is challenging due to unpredictable addressing caused by variable encoding rates, especially in neural network weight compression, which affects efficient neural network processing.
Innovation Solution
A processor generates position data to directly access and re-access specific positions within a compressed data stream, allowing efficient reading and decompression of data portions without requiring full decompression from the beginning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is compressed to reduce size, then storage efficiency and bandwidth usage improve, but random access to specific data within the compressed stream becomes difficult
Solution Approach 1:
The system performs preliminary decomposition of the compressed data stream into multiple segments and pre-calculates position information for each segment. This allows the processor to directly access specific data portions without decompressing the entire stream, resolving the contradiction between compression efficiency and random access capability.
Solution Approach 2:
The compressed data stream is divided into multiple segments with associated position information. Each segment can be independently accessed and decompressed, enabling efficient random access to specific portions of the compressed data while maintaining overall compression efficiency.
2Ease of operation
If the entire compressed data stream is decompressed from the beginning, then all data becomes accessible, but time and computational resources are wasted on data that has already been processed
Solution Approach 1:
Position information is pre-calculated and stored for each segment of the compressed data stream. When a specific portion is needed, the system directly jumps to the corresponding position using this pre-computed information, avoiding the need to re-decompress data from the beginning and significantly reducing decompression time.
Solution Approach 2:
The system creates and stores position metadata that acts as a copy or map of the compressed data stream structure. This metadata contains information about segment boundaries and positions, allowing direct navigation without processing the actual compressed data, thus eliminating redundant decompression operations.
3Quantity of substance
If neural network weights are stored in compressed format, then memory footprint is reduced, but accessing specific weights requires decompression overhead
Solution Approach 1:
Neural network weights are organized into segments within the compressed data stream, with each segment having associated position information. This allows the system to load only the specific weight segments needed for current processing, maintaining low memory footprint while enabling efficient access without decompressing unnecessary weight data.
Solution Approach 2:
Position information for weight segments is pre-calculated and stored alongside the compressed weight data. This enables direct access to specific weight portions during neural network processing, eliminating decompression overhead for weights that are already in memory or can be quickly retrieved.
Data Source
AI summary
A processor to generate position data indicative of a position within a compressed data stream, wherein, previously, in executing a task, data of the compressed data stream ending at the position has been read by the processor from storage storing the compressed data stream. After reading the data, the processor reads further data of the compressed data stream from the storage, in executing the task, the further data located beyond the position within the compressed data stream. After reading the further data, the processor reads, based on the position data, a portion of the compressed data stream from the storage, in executing the task, starting from the position within the compressed data stream. The processor decompresses the portion of the compressed data stream to generate decompressed data, in executing the task.