Arithmetic Circuit Serialization Using Lossless Arithmetic 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 lead to increased computational resources and bandwidth usage, especially when dealing with arithmetic circuits.
Innovation Solution
The implementation of arithmetic coding techniques to compress arithmetic circuits, allowing for a serialized representation that can be stored and transmitted more efficiently, reducing the data footprint and enabling lossless reproduction of the original circuit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If arithmetic circuits are stored in uncompressed form, then execution accuracy is maintained, but storage space and bandwidth requirements increase
Solution Approach 1:
The patent applies arithmetic coding to transform the representation parameters of arithmetic circuits from uncompressed binary form to compressed fractional form. By changing the encoding parameters and using interval-based representation, the circuit data achieves significant compression while maintaining lossless reconstructability, thus reducing storage space without compromising execution accuracy
Solution Approach 2:
The patent creates a compressed copy of the arithmetic circuit through arithmetic coding that can be stored and transmitted efficiently. This compressed representation serves as a surrogate that can be perfectly reconstructed into the original circuit when needed, enabling space-efficient storage while preserving full functionality
2Quantity of substance
If arithmetic circuits are compressed using arithmetic coding, then storage and bandwidth requirements decrease, but computational complexity increases
Solution Approach 1:
The patent performs arithmetic coding compression in advance before storage or transmission, converting the arithmetic circuit into a compressed serialized form. This preliminary compression action reduces the data footprint for subsequent storage and bandwidth usage, while the decoding can be performed efficiently when the circuit needs to be executed
Solution Approach 2:
The patent extracts only the essential information needed to represent the arithmetic circuit by applying arithmetic coding that identifies and encodes only the critical parameters and relationships. This extraction process removes redundant data while preserving the core computational logic, reducing complexity for storage and transmission
3Adaptability or versatility
If smart contracts are executed on blockchain nodes, then decentralization is maintained, but computational resources and energy consumption increase
Solution Approach 1:
The patent compresses arithmetic circuits using arithmetic coding before deploying them as smart contracts on blockchain nodes. By changing the representation parameters from uncompressed to compressed form, the data footprint is significantly reduced, which directly lowers storage requirements and bandwidth consumption across the decentralized network, thereby reducing overall energy usage while maintaining decentralization
Solution Approach 2:
The patent creates compressed copies of arithmetic circuits that are distributed across blockchain nodes. These compressed representations consume fewer computational resources for storage and validation, enabling more nodes to participate in the decentralized network without proportionally increasing energy consumption, thus preserving decentralization while improving energy efficiency
Data Source
AI summary
Techniques described herein may be utilized to implement methods and systems for lossless compression and serialization 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-serialization 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.