Lossless Data Compression Using Power-of-Two Entropy Coding
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Solution Overview
Problem
Existing lossless compression techniques for artificial intelligence workloads require computationally intensive operations, leading to significant power consumption and resource inefficiency in processing large volumes of data.
Innovation Solution
The use of power of two probability distribution functions to avoid costly mathematical operations, implementing efficient lossless compression and decompression methods that reduce power consumption and improve processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If computationally intensive compression techniques are used, then compression ratio is improved, but power consumption increases significantly
Solution Approach 1:
The patent changes the parameter of probability distribution from general distributions requiring complex calculations to power-of-two distributions where probabilities are powers of two. This allows compression operations to use simple bit shifts instead of multiplication/division, dramatically reducing power consumption while maintaining compression effectiveness
Solution Approach 2:
The patent replaces complex mathematical computation mechanisms (multiplication, division, floating-point operations) with simpler bitwise operations (shifts, masks). This substitution of computational mechanisms reduces the hardware complexity and power consumption of the compression system
2Loss of substance
If computationally intensive compression techniques are used, then compression capability is improved, but processing efficiency deteriorates
Solution Approach 1:
By changing the probability distribution parameters to powers of two, the patent enables compression algorithms to use efficient bitwise operations instead of computationally expensive arithmetic operations, significantly improving processing speed and efficiency while maintaining compression capability
Solution Approach 2:
The patent segments the compression process into discrete bitwise operations that can be executed efficiently in parallel or pipelined, improving overall processing throughput compared to sequential complex mathematical operations
Data Source
AI summary
Mechanisms are disclosed for performing efficient lossless encryption and decryption to reduce power consumption and improve the efficiency of processing streams of digital data. An input data stream associated with a stream of input data is received. A discrete wavelet transform is applied to a first serial input data value in the input data stream. Distributed components of the first serial input data value are distributed by computing a cumulative probability that the first serial input data value is less than or equal to a power-of-two range value associated with a power-of-two probability distribution function. An entropy encoded encryption value is computed based on a range variant asymmetrical numeral system based on the power-of-two probability distribution function. The entropy encoded encryption value is unloaded based on one or more computed next states. Compressed serial output data streams are compressed based on the unloaded entropy encoded encryption value.