ML System Architecture

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-3] ✅

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-3]
  • 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-3]

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.

This document follows the AI Labeling System standards based on the Bitcoin Development Framework v2.5.