インサイト - Machine Learning - # Translating Multi-Layer Perceptron Models from PyTorch to Solidity Smart Contracts

Deploying Multi-Layer Perceptron Models as Transparent and Verifiable Smart Contracts on the Blockchain

Q: How can the ML2SC translator be extended to support a wider range of ML model architectures beyond MLPs, such as Convolutional Neural Networks and Recurrent Neural Networks?

To extend the ML2SC translator to support a wider range of ML model architectures beyond Multi-Layer Perceptrons (MLPs), such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), several key steps can be taken: Model Architecture Understanding: The translator needs to be enhanced to understand the unique structures and operations of CNNs and RNNs. This includes convolutional layers, pooling layers, and recurrent connections in RNNs. Solidity Code Generation: The translator should be updated to generate Solidity code that can implement the specific operations of CNNs and RNNs. This involves translating convolutional operations, pooling operations, and recurrent connections into Solidity-compatible code. Data Handling: CNNs and RNNs often deal with multi-dimensional data. The translator should be modified to handle the input data format required by these architectures and ensure proper data manipulation within the Solidity smart contracts. Activation Functions: Different neural network architectures may use different activation functions. The translator should be flexible enough to support various activation functions commonly used in CNNs and RNNs. Testing and Validation: Extensive testing and validation are crucial to ensure the accuracy and performance of the translated models. The ML2SC translator should be tested with a variety of CNN and RNN models to verify the correctness of the translation. By incorporating these enhancements, the ML2SC translator can effectively support a broader range of ML model architectures, enabling developers to deploy complex neural network models on the blockchain with transparency and trust.

Q: What are the potential trade-offs between accuracy and gas cost that developers can explore when deploying ML models on the blockchain, and how can ML2SC be enhanced to provide better tuning capabilities?

When deploying ML models on the blockchain, developers often face trade-offs between accuracy and gas cost. These trade-offs can be managed and optimized through various strategies: Model Simplification: Simplifying the model architecture can reduce gas costs but may lead to a decrease in accuracy. Developers can explore optimizing the model complexity based on the available gas budget and desired accuracy level. Data Compression: Using data compression techniques can reduce the amount of data transferred on the blockchain, leading to lower gas costs. However, this compression may impact the accuracy of the model due to information loss. Gas-Efficient Operations: Choosing gas-efficient operations and algorithms can help minimize the overall gas cost of model deployment and inference. Developers can optimize the Solidity code generated by ML2SC to use gas-efficient constructs and minimize redundant computations. Gas Cost Monitoring: Developers can monitor and analyze the gas costs associated with different model configurations to identify areas where optimizations can be made. ML2SC can be enhanced to provide detailed gas cost breakdowns for different model architectures, enabling developers to make informed decisions. Parameter Tuning: ML2SC can be enhanced to provide tuning capabilities for developers to adjust model parameters based on their gas cost constraints. This can include options to fine-tune activation functions, layer sizes, and other hyperparameters to balance accuracy and gas efficiency. By offering better tuning capabilities and insights into the gas costs of deploying ML models on the blockchain, ML2SC can empower developers to make informed decisions and optimize their models for cost-effective and accurate performance.

核心概念

A novel PyTorch-to-Solidity translator, ML2SC, that can automatically deploy Multi-Layer Perceptron (MLP) models on the blockchain, providing transparency and verifiability of model inference.

要約

The paper introduces ML2SC, a tool that can automatically translate Multi-Layer Perceptron (MLP) models written in PyTorch to Solidity smart contracts. This allows deploying ML models on the blockchain, ensuring transparency and verifiability of the model inference process.

The key highlights are:

ML2SC uses a fixed-point math library (PRBMath) to approximate floating-point computations in Solidity, enabling identical performance to the original off-chain PyTorch models.
The paper provides a detailed mathematical modeling of the gas costs associated with deploying, updating, and running inference on the on-chain MLP models. The gas costs are shown to increase linearly with various model architecture parameters.
Empirical results are presented, validating the accuracy parity between the on-chain Solidity models and the off-chain PyTorch implementations. The gas cost experiments also match the proposed mathematical models.
The authors offer ML2SC as an open-source tool to bridge the gap between theoretical ML models and their real-world deployment on blockchain platforms, contributing to the ongoing research in this field.

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統計

The weights in the MLP models range from about -2.73 to 3.12.
The test dataset contains 50 data points.

引用

"Our evaluation shows that on-chain models and their off-chain counterparts have identical performance when tested on the same test set."
"PRBMath's 58.19 decimal fixed-point is more than capable of representing neuron weights and performing calculations on them that match what is obtained with Pytorch."

抽出されたキーインサイト

ML2SC: Deploying Machine Learning Models as Smart Contracts on the Blockchain

by Zhikai Li,St... 場所 arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.16967.pdf

ML2SC: Deploying Machine Learning Models as Smart Contracts on the Blockchain

深掘り質問

How can the ML2SC translator be extended to support a wider range of ML model architectures beyond MLPs, such as Convolutional Neural Networks and Recurrent Neural Networks?

To extend the ML2SC translator to support a wider range of ML model architectures beyond Multi-Layer Perceptrons (MLPs), such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), several key steps can be taken:

Model Architecture Understanding: The translator needs to be enhanced to understand the unique structures and operations of CNNs and RNNs. This includes convolutional layers, pooling layers, and recurrent connections in RNNs.

Solidity Code Generation: The translator should be updated to generate Solidity code that can implement the specific operations of CNNs and RNNs. This involves translating convolutional operations, pooling operations, and recurrent connections into Solidity-compatible code.

Data Handling: CNNs and RNNs often deal with multi-dimensional data. The translator should be modified to handle the input data format required by these architectures and ensure proper data manipulation within the Solidity smart contracts.

Activation Functions: Different neural network architectures may use different activation functions. The translator should be flexible enough to support various activation functions commonly used in CNNs and RNNs.

Testing and Validation: Extensive testing and validation are crucial to ensure the accuracy and performance of the translated models. The ML2SC translator should be tested with a variety of CNN and RNN models to verify the correctness of the translation.

By incorporating these enhancements, the ML2SC translator can effectively support a broader range of ML model architectures, enabling developers to deploy complex neural network models on the blockchain with transparency and trust.

What are the potential trade-offs between accuracy and gas cost that developers can explore when deploying ML models on the blockchain, and how can ML2SC be enhanced to provide better tuning capabilities?

When deploying ML models on the blockchain, developers often face trade-offs between accuracy and gas cost. These trade-offs can be managed and optimized through various strategies:

Model Simplification: Simplifying the model architecture can reduce gas costs but may lead to a decrease in accuracy. Developers can explore optimizing the model complexity based on the available gas budget and desired accuracy level.

Data Compression: Using data compression techniques can reduce the amount of data transferred on the blockchain, leading to lower gas costs. However, this compression may impact the accuracy of the model due to information loss.

Gas-Efficient Operations: Choosing gas-efficient operations and algorithms can help minimize the overall gas cost of model deployment and inference. Developers can optimize the Solidity code generated by ML2SC to use gas-efficient constructs and minimize redundant computations.

Gas Cost Monitoring: Developers can monitor and analyze the gas costs associated with different model configurations to identify areas where optimizations can be made. ML2SC can be enhanced to provide detailed gas cost breakdowns for different model architectures, enabling developers to make informed decisions.

Parameter Tuning: ML2SC can be enhanced to provide tuning capabilities for developers to adjust model parameters based on their gas cost constraints. This can include options to fine-tune activation functions, layer sizes, and other hyperparameters to balance accuracy and gas efficiency.

By offering better tuning capabilities and insights into the gas costs of deploying ML models on the blockchain, ML2SC can empower developers to make informed decisions and optimize their models for cost-effective and accurate performance.

Given the constraints of current blockchain platforms, what alternative L1 or L2 blockchain solutions could be explored to provide more cost-effective and accurate blockchain-based machine learning capabilities?

To overcome the constraints of current blockchain platforms and enhance the cost-effectiveness and accuracy of blockchain-based machine learning capabilities, exploring alternative L1 or L2 blockchain solutions is crucial. Some potential alternatives include:

Polkadot: Polkadot is a multi-chain blockchain platform that allows for interoperability between different blockchains. By leveraging Polkadot's parachain architecture, developers can access specialized blockchains optimized for machine learning tasks, enhancing performance and cost-effectiveness.

Solana: Solana is a high-performance blockchain platform known for its scalability and low transaction costs. By utilizing Solana's fast transaction speeds and low fees, developers can deploy machine learning models more efficiently and cost-effectively compared to traditional platforms like Ethereum.

NEAR Protocol: NEAR Protocol is a developer-friendly blockchain platform that offers low gas fees and fast transaction finality. Developers can explore NEAR Protocol for deploying machine learning models on the blockchain with reduced costs and improved performance.

Binance Smart Chain (BSC): Binance Smart Chain is a blockchain platform that provides compatibility with Ethereum Virtual Machine (EVM) and lower transaction fees. By deploying machine learning models on BSC, developers can benefit from cost-effective transactions and seamless integration with existing Ethereum tools.

Polygon (formerly Matic Network): Polygon is a layer 2 scaling solution for Ethereum that offers fast and low-cost transactions. By utilizing Polygon's sidechain infrastructure, developers can deploy machine learning models on a scalable and cost-effective platform while maintaining compatibility with Ethereum.

Exploring these alternative L1 and L2 blockchain solutions can provide developers with more options to deploy machine learning models efficiently, overcome the limitations of current platforms, and achieve cost-effective and accurate blockchain-based machine learning capabilities.