Arithmetic Circuit Serialization Using Lossless Entropy Coding
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Solution Overview
Problem
Current blockchain technologies face challenges in efficiently storing and executing smart contracts due to high data storage requirements, which can be exacerbated by the need for large arithmetic circuits, leading to increased computational resources and storage needs.
Innovation Solution
The implementation of arithmetic coding techniques to compress arithmetic circuits, allowing for the generation of a serialized circuit that can be stored more compactly and executed efficiently, reducing storage and computational demands by mapping symbols to coded values and utilizing entropy coding schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If arithmetic circuits are stored in uncompressed form, then execution accuracy is maintained, but storage space requirements increase
Solution Approach 1:
The patent applies arithmetic coding to transform the representation parameters of arithmetic circuits from uncompressed symbolic form to compressed numerical form. By mapping circuit symbols to numerical codes and using arithmetic coding algorithms, the storage parameters are optimized while maintaining lossless reconstruction capability, thus reducing storage space without compromising execution accuracy
Solution Approach 2:
The patent creates compressed copies of arithmetic circuits through arithmetic coding. These compressed representations serve as efficient storage copies that can be perfectly reconstructed into the original circuit form when needed for execution, enabling space-efficient storage while preserving complete execution fidelity
2Volume of stationary object
If arithmetic circuits are compressed using arithmetic coding, then storage space is reduced, but computational complexity increases
Solution Approach 1:
The patent performs arithmetic coding compression in advance during circuit compilation or deployment phases. This preliminary compression action reduces the storage burden for subsequent executions, trading off initial computational effort for long-term storage efficiency and faster retrieval times
Solution Approach 2:
The patent introduces arithmetic coding algorithms as an intermediary transformation layer between the original arithmetic circuit representation and its stored form. This intermediary encoding process enables efficient storage while maintaining the ability to perfectly reconstruct the original circuit, managing the trade-off between compression overhead and storage efficiency
3Adaptability or versatility
If large arithmetic circuits are used for smart contracts, then functionality is enhanced, but data transmission efficiency decreases
Solution Approach 1:
The patent transforms the data representation parameters of arithmetic circuits through arithmetic coding, enabling compact encoding of complex smart contract logic. This parameter transformation maintains complete functionality while dramatically reducing the size of data transmitted across the blockchain network
Solution Approach 2:
The patent creates compressed copies of arithmetic circuits for transmission. These compressed representations can be efficiently transmitted across the network and then perfectly reconstructed at the destination, enabling fast transmission of complex smart contracts without losing any functional capability
Data Source
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AI summary
Techniques described herein may be utilised to implement methods and systems for lossless compression and serialisation of arithmetic circuits to a bit stream using compression techniques such as the arithmetic coding. An arithmetic circuit representing a smart contract may be compressed using arithmetic coding, thereby generating a compressed arithmetic circuit that can be stored or broadcast to a blockchain network using less computational resources (e.g., data storage resources) than would otherwise be needed to store the arithmetic circuit. The arithmetic circuit can be efficiently compressed using entropy coding based on the frequency of elements in the data structure, such as the arithmetic operator types. Instructions for de-serialisation and de-compression can also be embedded in the bit stream, and can be used (e.g., by another computer system) to reconstruct the original circuit in a lossless manner.