AI

MetisVM provides foundational support for on-chain AI applications through a set of innovative technologies designed to make AI-driven decentralized applications feasible and efficient. This documentation covers how developers can leverage MetisVM's AI infrastructure to build intelligent blockchain applications.

Core AI Capabilities

MetisVM enables three critical capabilities for AI on blockchain:

1. Efficient On-Chain Inference: Execute AI models directly within smart contracts
2. Secure Model Verification: Verify model inputs, outputs, and execution
3. Performance Optimization: Special opcodes and execution patterns for AI workloads

Inference Engine Optimization

MetisVM optimizes smart contract execution for AI workloads through specialized inference engines:

Key Components

• VM Precompilation: Pre-optimized execution paths for common AI operations
• Host Functions: Low-level optimized functions for matrix calculations
• Quantization Support: Efficient handling of quantized AI models
• Custom Opcodes: Extended instruction set for AI-specific operations

AI Coprocessor Acceleration

MetisVM can utilize various hardware accelerators to enhance machine learning inference performance:

Supported Acceleration Technologies

• SIMD Instructions: Vector processing using AVX512
• GPU Acceleration: Offloading compute-intensive operations
• TPU Integration: Specialized AI processing units
• FPGA Support: Customizable hardware acceleration

Accessing Accelerated Computation

zkVM Integration

MetisVM supports integration with zkVM (Zero-Knowledge Virtual Machine) to generate zero-knowledge proofs for AI inference:

Key Features

• Private Inference: Run AI models on private data with public verification
• Model Integrity: Prove correct model execution without revealing the model
• Verifiable Results: Generate proofs that inference was performed correctly

Developer Toolkit for AI

MetisVM delivers a comprehensive toolkit to streamline AI integration for blockchain developers:

Security Considerations

Model Security

When deploying AI models on-chain, consider these security aspects:

• Model Ownership: Who controls model updates and changes
• Adversarial Attacks: Resistance to input manipulation
• Oracle Reliance: Dependencies on external data sources
• Versioning: Managing model upgrades and backward compatibility

Best Practices

• Model Auditing: Verify model behavior across a comprehensive test suite
• Gradual Rollout: Phase in AI capabilities with circuit breakers
• Governance: Establish clear processes for model updates
• Monitoring: Track on-chain model performance and detect anomalies

Future Roadmap

The AI infrastructure support in MetisVM will continue to evolve with:

• Advanced Model Compression: Further optimizations for on-chain deployment
• Multi-modal Support: Handling of images, text, and other data types
• Federated Learning: Distributed model training with privacy preservation
• Cross-chain AI: Models that can operate across multiple blockchains
• AI-specific Governance: Specialized mechanisms for AI model management

Getting Started

Prerequisites

• MetisVM development environment
• Basic understanding of smart contracts
• Familiarity with at least one ML framework (PyTorch, TensorFlow, etc.)

Conclusion

MetisVM's AI infrastructure support represents a significant advancement in blockchain capabilities, enabling a new generation of intelligent decentralized applications. By combining optimized execution, hardware acceleration, and zero-knowledge technology, MetisVM creates an environment where AI and blockchain can seamlessly converge, unlocking powerful new use cases across finance, gaming, security, and beyond.