ML System Architecture

Table of Contents

Last Updated: 2025-03-06

Overview

Anya Core's Machine Learning system provides advanced AI capabilities for the platform, including Bitcoin analytics, security monitoring, and system intelligence. The ML system follows a hexagonal architecture pattern with clearly defined inputs, outputs, and domain logic.

System Components

1. ML*/Agent Checker System (AIP-002) ✅

The Agent Checker system is a critical component that monitors and verifies the health and readiness of all system components. It uses ML-based analysis to determine component status and system stage.

Key Features:

  • System stage management (Development: 60%, Production: 90%, Release: 99%)
  • Component readiness assessment with detailed metrics
  • Input monitoring and analysis
  • Auto-save functionality that persists state after every 20th input
  • Thread-safe implementation with proper locking

Implementation:

  • Location: src/ml/agent_checker.rs
  • AI Label: AIP-002
  • Status: ✅ Complete
  • Auto-Save: Enabled (every 20th input)

Component States:

pub enum SystemStage {
    Development,  // 60% threshold
    Production,   // 90% threshold
    Release,      // 99% threshold
    Unavailable,  // Below threshold
}

Architecture:

┌────────────────────┐    ┌─────────────────────┐    ┌────────────────────┐
│                    │    │                     │    │                    │
│   Input Sources    │───▶│   Agent Checker     │───▶│   System Actions   │
│                    │    │                     │    │                    │
└────────────────────┘    └─────────────────────┘    └────────────────────┘
                               │       ▲
                               │       │
                               ▼       │
                          ┌────────────────┐
                          │                │
                          │    In-Memory   │
                          │    State       │
                          │                │
                          └────────────────┘

2. Model Management

The Model Management component handles ML model deployment, versioning, and lifecycle. Models can be loaded, updated, and managed through a unified interface.

Key Features:

  • Model versioning and tracking
  • Model loading and initialization
  • Model metadata management
  • Model evaluation and performance tracking

3. Inference Engine

The Inference Engine executes ML models and provides prediction capabilities to the system.

Key Features:

  • Real-time inference
  • Batch processing
  • Hardware acceleration (GPU/NPU)
  • Model optimization

4. Performance Monitoring (AIR-008) ✅

The Performance Monitoring component tracks ML model and system performance metrics.

Key Features:

  • Resource monitoring (CPU, Memory, Network, etc.)
  • Performance metrics tracking (utilization, throughput, latency)
  • Target-based optimization
  • Auto-save functionality for configuration changes

Implementation:

  • Location: src/core/performance_optimization.rs
  • AI Label: AIR-008
  • Status: ✅ Complete
  • Auto-Save: Enabled (every 20th change)

5. Federated Learning

The Federated Learning component enables distributed model training across nodes.

Key Features:

  • Local model training
  • Model aggregation
  • Privacy-preserving learning
  • Model distribution

Auto-Save Implementation

All ML components with state management include auto-save functionality with the following characteristics:

  • Configurable auto-save frequency (default: every 20th input/change)
  • In-memory state persistence without file I/O
  • Thread-safe implementation with proper locking
  • Input counting and tracking
  • Timestamp-based save verification
// Example auto-save implementation (simplified)
fn record_input_and_check_save(&self) {
    let mut counter = self.input_counter.lock().unwrap();
    *counter += 1;

    // Auto-save every Nth input
    if *counter % self.auto_save_frequency == 0 {
        self.save_state_to_memory();
    }
}

fn save_state_to_memory(&self) {
    // Update last_save timestamp
    let mut last_save = self.last_save.lock().unwrap();
    *last_save = Instant::now();

    // State is kept in memory (no file I/O)
}

Data Flow

┌─────────────┐    ┌──────────────┐    ┌─────────────┐    ┌─────────────┐
│             │    │              │    │             │    │             │
│ Data Source │───▶│ Data Pipeline│───▶│ ML Processing│───▶│ Data Sink   │
│             │    │              │    │             │    │             │
└─────────────┘    └──────────────┘    └─────────────┘    └─────────────┘
                                           │    ▲
                                           │    │
                                           ▼    │
                                      ┌────────────────┐
                                      │                │
                                      │  Model Store   │
                                      │                │
                                      └────────────────┘

System Interfaces

Input Ports

  • Data ingestion endpoints
  • Model registration API
  • Training data interface
  • System metric collectors

Output Ports

  • Prediction API
  • Model performance metrics
  • System health indicators
  • Alerting and notification

Implementation Details

Core ML Components

  • MLSystem - Main ML system manager
  • MLModel - Model interface
  • MLService - Service layer
  • AgentChecker - System verification component (AIP-002)
  • PerformanceOptimizer - Performance monitoring and optimization (AIR-008)

Technology Stack

  • TensorFlow / PyTorch for model training and inference
  • ONNX for model interoperability
  • Rust for system components
  • Python for model development
  • CUDA/ROCm for GPU acceleration
  • Custom tensors for RISC-V

Integration with Other Components

Security Integration

The ML system integrates with the Security Architecture to ensure:

  • Secure model storage and processing
  • Access control for model operations
  • Audit logging for ML operations
  • Threat detection in ML inputs/outputs

Performance Integration

The ML system integrates with the Performance Architecture to:

  • Monitor resource usage of ML components
  • Optimize ML model execution
  • Control scaling of ML operations
  • Ensure efficient resource utilization

Core System Integration

The ML system integrates with the Core System to:

  • Process input through the AgentChecker
  • Receive global configuration from the core system
  • Report system health to the core system
  • Coordinate operations with other components

Testing Strategy

The ML system includes comprehensive testing:

  1. Unit Tests: For individual components and functions
  2. Integration Tests: For component interaction
  3. Performance Tests: For model performance and scalability
  4. System Tests: For end-to-end verification

Security Considerations

  • Model input validation
  • Data privacy protection
  • Access control for model operations
  • Secure model storage
  • Attack prevention (model poisoning, adversarial examples)

Performance Benchmarks

Performance metrics for the ML system:

Component Latency (ms) Throughput (req/s) Memory (MB)
Inference Engine 15-50 100-500 200-500
Model Loading 200-1000 N/A 50-200
Agent Checker 5-10 1000+ 10-50
Performance Monitor 1-5 2000+ 5-20

Future Enhancements

  1. Enhanced ML model with more sophisticated pattern recognition
  2. Cloud-based metrics storage for long-term analysis
  3. Predictive capabilities for proactive component management
  4. Advanced anomaly detection in system behavior
  5. Automated optimization of system resources

Bitcoin-Specific ML Features

The ML system includes specialized features for Bitcoin operations that leverage our P1 components:

1. Transaction Analysis

  • Pattern recognition in transaction flows
  • Anomaly detection in blockchain data
  • Fee estimation optimization with adaptive learning
  • Block propagation prediction using network metrics

2. Agent Checker Bitcoin Integration

The Agent Checker system specifically monitors Bitcoin-related components:

  • Node Status Monitoring: Verifies connection status to Bitcoin nodes
  • Blockchain Sync Status: Tracks blockchain synchronization progress
  • Transaction Pool Monitoring: Analyzes mempool health and size
  • UTXO Set Analysis: Monitors UTXO set size and growth patterns

3. Security Component Integration

Our ML-based security features integrate with Bitcoin operations:

  • Fraud Detection: ML models identify suspicious transaction patterns
  • Double-Spend Prevention: Real-time analysis of transaction propagation
  • Network Partition Detection: Identifies potential network splits
  • Resource Attack Prevention: Detects and mitigates resource exhaustion attacks

4. Performance Optimization for Bitcoin Operations

The Performance Optimizer specifically enhances Bitcoin operations:

  • Node Performance Tuning: Optimizes resource allocation for Bitcoin nodes
  • Transaction Validation Acceleration: Improves transaction verification speed
  • Block Processing Optimization: Enhances block validation and propagation
  • Network Bandwidth Management: Optimizes P2P network communication

5. Layer 2 Support

The ML system now includes specialized support for Bitcoin Layer 2 solutions:

  • BOB Integration: Support for the BOB hybrid L2 rollup
  • Bitcoin Relay Monitoring: Tracking the health and status of BOB's Bitcoin relay
  • Smart Contract Analysis: ML-based monitoring of Bitcoin-interacting smart contracts
  • Cross-Layer Transaction Verification: Verifying transactions across Bitcoin and BOB layers
  • BitVM Optimization: Enhancing BitVM verification processes through ML-driven optimizations

  • Hybrid Stack Analytics: Analyzing transaction patterns across the hybrid stack

  • Lightning Network Analytics: Monitoring channel health and liquidity

  • Sidechains Monitoring: Tracking two-way peg mechanisms and validation
  • State Channel Analysis: Optimizing state channel opening/closing efficiency

6. Auto-Save for Bitcoin State

The auto-save functionality preserves critical Bitcoin operation state:

  • Mempool State: Preserves pending transaction information
  • Peer Connection Status: Maintains network topology information
  • Validation Progress: Saves block validation progress
  • Resource Utilization: Tracks resource usage patterns for Bitcoin operations
  • Layer 2 State: Preserves the state of Layer 2 networks and their interactions with the main chain

This integration ensures that our ML*/Agent Checker system provides comprehensive monitoring and optimization for Bitcoin operations while maintaining the system's security and performance across all layers of the Bitcoin ecosystem.

[AIR-3][AIS-3][BPC-3][RES-3]

For more details on how AI components are labeled, see the AI Labeling System.

See Also