Parallel Decompression Lookup Table for AI Data Bandwidth
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Solution Overview
Problem
Current data compression methods, such as Huffman compression, are inadequate for meeting the real-time data reading and writing requirements of AI engines due to slow decompression speeds, as they rely on serialized processing of bit values.
Innovation Solution
A data compression method that establishes an initial lookup table using data with the same numerical values as indexes, performs sequential encoding operations, and adds a separator to the encoding results to create a target lookup table for efficient compression, allowing parallel decompression processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If Huffman compression algorithm is used to compress data, then bandwidth utilization rate is improved, but decompression speed deteriorates and cannot meet real-time AI engine requirements
Solution Approach 1:
The patent pre-calculates and stores encoding mappings for all possible input values in a lookup table during system initialization. This preliminary action eliminates the need for sequential bit-by-bit decoding during runtime, allowing AI engines to directly retrieve decoded values through parallel table lookups, thus resolving the contradiction between compression efficiency and decompression speed
Solution Approach 2:
The patent replaces the mechanical sequential processing mechanism of Huffman decoding with a parallel lookup table mechanism. Instead of processing bits sequentially through tree traversal, the system substitutes this with direct memory access to pre-computed encoding tables, enabling parallel decomposition of data batches and achieving real-time processing speeds required by AI engines
2Reliability
If sequential processing is used to decompress data, then decompression accuracy is maintained, but processing efficiency deteriorates and cannot handle large data volumes in real time
Solution Approach 1:
The patent segments the decompression process into independent parallel operations by dividing the compressed data into multiple batches, where each batch can be decompressed simultaneously through lookup table queries. This segmentation maintains accuracy for each individual element while dramatically improving overall processing efficiency through parallel execution
Solution Approach 2:
The patent merges multiple sequential decoding operations into a single parallel lookup operation. By combining the decoding logic for all possible input values into a unified lookup table structure, the system can process multiple data elements simultaneously through a single memory access pattern, thereby maintaining reliability while achieving high productivity
Data Source
AI summary
A data compression, decompression method, and an electronic device. The method includes the following steps: establishing an initial lookup table by using data with the same value in dataset to be compressed as one index, sequentially building a new Huffman tree corresponding to each index, and then adding a separator to obtain an encoding list containing a target encoding value and length, adding the encoding list to the initial lookup table to obtain a target lookup table. According to a separator of bitstream data, and searching the target lookup table in parallel, and use the indexes to obtain the decompression result of the data to be decompressed. Embodiments can perform a parallel decompression operation to increase a decompression speed, so that the decompression speed can meet the requirement of an AI engine for a large amount of weight data bandwidth in real time.