Integer Encoding Circuit for Low-Latency Lossless Compression
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Solution Overview
Problem
Conventional lossless data compression methods are either computationally expensive or fail to achieve high compression ratios with high throughput at low latency, making them inefficient for various data processing systems.
Innovation Solution
The development of systems that utilize encoding and decoding techniques such as SIGBITS, SIGBYTES, and LFLR for lossless data compression, which convert data-tokens into encoded and packed representations, allowing for efficient compression and decompression of integers and symbols while maintaining high compression ratios, high throughput, low latency, low energy consumption, and low implementation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If conventional lossless data compression methods are used, then compression ratio is improved, but computational complexity increases and throughput decreases
Solution Approach 1:
The patent segments the 32-bit integer encoding process into distinct functional units: a priority encoder that identifies the position of the most significant bit, a barrel shifter that performs bit shifting operations, and a multiplexer that selects between different encoding paths. This segmentation allows each component to be optimized independently and enables parallel processing of multiple integers simultaneously, thereby reducing overall computational complexity while maintaining high compression ratios.
Solution Approach 2:
The patent replaces complex software-based compression algorithms with dedicated hardware circuitry. By implementing the encoding logic in hardware components such as priority encoders, barrel shifters, and multiplexers, the system achieves significantly higher throughput and lower latency compared to conventional software-based compression methods, while maintaining the same compression effectiveness.
2Loss of substance
If conventional lossless data compression methods are used, then compression ratio is improved, but latency increases
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing encoding tables for common integer patterns, and by using a priority encoder that immediately identifies the most significant bit position upon receiving input. This allows the encoding process to begin without waiting for complete data analysis, significantly reducing latency while maintaining compression effectiveness.
Solution Approach 2:
The patent ensures continuity of useful action through pipelined processing where multiple encoding operations occur simultaneously at different stages. The hardware architecture allows one integer to be encoded while another is being processed, eliminating idle time and maintaining continuous throughput, thereby reducing overall latency for processing sequences of integers.
3Loss of substance
If conventional lossless data compression methods are used, then compression ratio is improved, but energy consumption increases
Solution Approach 1:
The patent extracts and eliminates redundant computational steps found in conventional compression algorithms. By using a priority encoder to directly identify the most significant bit position and a barrel shifter to perform optimized bit shifting, the system removes unnecessary intermediate calculations and data transformations, thereby reducing energy consumption while achieving the same compression ratio.
Solution Approach 2:
The patent changes the operational parameters of the encoding process by using fixed-width hardware components with deterministic timing and power consumption characteristics. The barrel shifter is designed to perform a specific number of shift operations based on the priority encoder output, eliminating variable-time and variable-power operations typical of software algorithms, thus reducing overall energy consumption.
Data Source
AI summary
A system for encoding and decoding data-tokens. In some examples, the system may be configured to encode and decode integers. In other cases, the system may be configured to encode and decode symbols or bytes of data.


