Dragonfire Engine

Alpha

The Dragonfire Engine is the high-performance processing core of the DragonFire Semantic Network, operating at an unprecedented 65,536 frames per second. It manages bitstreams on semantic ports, applies transformations, and implements pattern detection for real-time data processing at scale.

Developer Note: The Dragonfire Engine is currently in Alpha. APIs may change before final release.

Core Concepts

Bitstreams

The fundamental unit of data processing in the Dragonfire Engine is the bitstream—a high-performance sequence of bits flowing through semantic ports. Each bitstream is associated with a specific port number that defines its geometric properties and processing characteristics.

XOR Transformations

Bitstream data is processed using XOR transformations—high-performance bitwise operations that can be applied using SIMD (Single Instruction, Multiple Data) instructions for parallel processing. These transformations enable complex pattern matching, data fusion, and signal detection.

Fractal Tokens

To optimize repeated patterns in bitstreams, the engine generates fractal tokens that encode repetitive structures. This approach dramatically reduces bandwidth requirements and enables efficient pattern recognition across large datasets.

Implementation

The following example demonstrates the core implementation of the Dragonfire Engine:

class DragonFireEngine {
  constructor() {
    this.frameRate = 65536; // frames per second
    this.frameDuration = 1000000 / this.frameRate; // in microseconds
    this.streams = new Map(); // active bitstreams
    this.xorMasks = new Map(); // precomputed transformation masks
    this.frameCount = 0n; // 64-bit frame counter
  }
  
  // Initialize a bitstream on a semantic port
  initStream(streamId, port) {
    this.streams.set(streamId, {
      port,
      buffer: new Uint8Array(8192), // 65536 bits
      position: 0,
      lastUpdate: this.frameCount
    });
  }
  
  // Apply XOR transform to a stream
  applyXORTransform(streamId, maskId) {
    const stream = this.streams.get(streamId);
    const mask = this.xorMasks.get(maskId);
    
    if (!stream || !mask) return false;
    
    // Fast XOR using SIMD if available
    if (typeof SIMD !== 'undefined') {
      // Use SIMD.Int8x16 for parallel XOR operations
      for (let i = 0; i < stream.buffer.length; i += 16) {
        const streamVector = SIMD.Int8x16.load(stream.buffer, i);
        const maskVector = SIMD.Int8x16.load(mask, i);
        const result = SIMD.Int8x16.xor(streamVector, maskVector);
        SIMD.Int8x16.store(stream.buffer, i, result);
      }
    } else {
      // Fallback to regular XOR
      for (let i = 0; i < stream.buffer.length; i++) {
        stream.buffer[i] ^= mask[i % mask.length];
      }
    }
    
    return true;
  }
  
  // Process a single engine frame
  processFrame() {
    this.frameCount++;
    
    // Process all active streams for this frame
    this.streams.forEach((stream, streamId) => {
      // Apply scheduled transformations
      if (this.hasScheduledTransform(streamId, this.frameCount)) {
        const transformations = this.getScheduledTransforms(streamId, this.frameCount);
        for (const transform of transformations) {
          this.applyXORTransform(streamId, transform.maskId);
        }
      }
    });
  }
  
  // Generate a fractal compression token for repeating patterns
  generateFractalToken(streamId, startPos, length, iterations) {
    const stream = this.streams.get(streamId);
    if (!stream) return null;
    
    // Create a fractal token that describes repeating pattern
    return {
      type: 'fractal',
      streamId,
      pattern: stream.buffer.slice(startPos, startPos + length),
      iterations,
      transform: 'xor' // Could be different transform types
    };
  }
  
  // Real-time processing loop - runs at 65536 fps
  startProcessing() {
    const frameInterval = Math.floor(this.frameDuration / 1000); // in ms
    
    // Use high-resolution timer if available
    if (typeof window !== 'undefined' && window.requestAnimationFrame) {
      const processFrameLoop = () => {
        this.processFrame();
        window.requestAnimationFrame(processFrameLoop);
      };
      window.requestAnimationFrame(processFrameLoop);
    } else {
      // Fallback to setInterval (less precise)
      setInterval(() => this.processFrame(), frameInterval);
    }
  }
}

Working with the Dragonfire Engine

Creating Transformation Masks

Transformation masks are the key to implementing specific operations on bitstreams. Here's an example of creating a logical AND operation using XOR masks:

// Create a transformation mask that implements logical AND
const createANDMask = (engine, stream1Id, stream2Id, resultId) => {
  const stream1 = engine.streams.get(stream1Id);
  const stream2 = engine.streams.get(stream2Id);
  const resultStream = engine.streams.get(resultId);
  
  if (!stream1 || !stream2 || !resultStream) return false;
  
  // Copy stream1 to result
  for (let i = 0; i < stream1.buffer.length; i++) {
    resultStream.buffer[i] = stream1.buffer[i];
  }
  
  // Create mask that, when XORed with stream1, will produce:
  // 1 only when stream1 AND stream2 are both 1
  const mask = new Uint8Array(8192);
  for (let i = 0; i < mask.length; i++) {
    // XOR with this mask will zero out bits where stream2 is 0
    mask[i] = stream1.buffer[i] & ~stream2.buffer[i];
  }
  
  // Register the mask
  engine.xorMasks.set('and_mask', mask);
  
  // Apply the mask to produce AND result
  engine.applyXORTransform(resultId, 'and_mask');
  
  return true;
};

Pattern Detection

The Dragonfire Engine can be extended with pattern detection functionality to identify specific patterns in bitstreams:

// Add pattern detection method
DragonFireEngine.prototype.registerPatternDetector = function(streamId, detector) {
  if (!this.patternDetectors) {
    this.patternDetectors = new Map();
  }
  this.patternDetectors.set(streamId, detector);
};

// Method to check pattern
DragonFireEngine.prototype.checkPattern = function(detectorId, data, offset, length) {
  const detector = this.patternDetectors.get(detectorId);
  if (!detector) return false;
  
  return detector(data, offset, length);
};

// Example: Register a threshold detector
engine.registerPatternDetector('threshold_detector', (data, offset, length) => {
  const threshold = 0x7F;
  for (let i = offset; i < offset + length; i++) {
    if (data[i] > threshold) {
      return true; // Pattern detected
    }
  }
  return false;
});

Frame Handlers

To execute custom logic on specific frames, you can register frame handlers:

// Add frame handler registration
DragonFireEngine.prototype.registerFrameHandler = function(handler) {
  if (!this.frameHandlers) {
    this.frameHandlers = [];
  }
  this.frameHandlers.push(handler);
};

// Modify processFrame to call handlers
const originalProcessFrame = DragonFireEngine.prototype.processFrame;
DragonFireEngine.prototype.processFrame = function() {
  // Call original implementation
  originalProcessFrame.call(this);
  
  // Call frame handlers
  if (this.frameHandlers) {
    for (const handler of this.frameHandlers) {
      handler(this.frameCount);
    }
  }
};

// Example: Register a frame handler that runs every 100 frames
engine.registerFrameHandler((frameNumber) => {
  if (frameNumber % 100 === 0) {
    console.log(`Processing frame ${frameNumber}`);
    // Execute custom processing...
  }
});

Performance Considerations

The Dragonfire Engine operates at an extremely high frame rate, which requires careful performance optimization:

SIMD Operations: Use SIMD when available for maximum throughput on bitstream operations
Typed Arrays: Always use typed arrays (Uint8Array, etc.) rather than regular JavaScript arrays
Frame Rate Adaptation: Consider implementing dynamic frame rate adjustment based on system capabilities
Worker Threads: For computationally intensive operations, offload to Web Workers
Selective Processing: Process only the data that has changed since the last frame

Example: Real-time Data Fusion

The following example demonstrates using the Dragonfire Engine for real-time sensor data fusion:

// Setup real-time sensor processing
const setupSensorFusion = (engine, timeSync) => {
  // Initialize bitstreams for sensor data
  engine.initStream('vibration', 500); // Port 500: Vibration data
  engine.initStream('temperature', 501); // Port 501: Temperature data
  engine.initStream('alarm', 510); // Port 510: Alarm output
  
  // Create alert detection transform
  const createAlertDetectionMask = () => {
    // This XOR-based mask detects when both streams have a 1 bit in the same position
    // Effectively implementing an AND operation
    const mask = new Uint8Array(8192);
    // Populate with pattern that results in 1 only when both inputs are 1
    // ... implementation details ...
    return mask;
  };
  
  // Register the mask
  engine.xorMasks.set('alertDetection', createAlertDetectionMask());
  
  // Setup pattern matching for vibration threshold
  engine.registerPatternDetector('vibration', (data, offset, length) => {
    // Check if vibration data exceeds threshold
    const threshold = 0x7F; // Example threshold value
    for (let i = offset; i < offset + length; i++) {
      if (data[i] > threshold) {
        return true; // Threshold exceeded
      }
    }
    return false;
  });
  
  // Setup pattern matching for temperature threshold
  engine.registerPatternDetector('temperature', (data, offset, length) => {
    // Check if temperature data exceeds threshold
    const threshold = 0x50; // Example threshold value
    for (let i = offset; i < offset + length; i++) {
      if (data[i] > threshold) {
        return true; // Threshold exceeded
      }
    }
    return false;
  });
  
  // Setup alarm fusion logic
  engine.registerFrameHandler(async (frameNumber) => {
    // Only process every 10 frames (still ~6.5kHz)
    if (frameNumber % 10 !== 0) return;
    
    const vibrationStream = engine.streams.get('vibration');
    const temperatureStream = engine.streams.get('temperature');
    const alarmStream = engine.streams.get('alarm');
    
    // Check if both thresholds are exceeded
    const vibrationAlert = engine.checkPattern('vibration', vibrationStream.buffer, 0, 64);
    const temperatureAlert = engine.checkPattern('temperature', temperatureStream.buffer, 0, 64);
    
    if (vibrationAlert && temperatureAlert) {
      // Set alarm bit to 1
      alarmStream.buffer[0] = 0x01;
      
      // Log alarm with precise time
      const alarmTime = timeSync.getSyncedMicroTime();
      console.log(`ALERT detected at μs ${alarmTime}, frame ${frameNumber}`);
      
      // Send alarm notification on real-time notification port
      await portalConnection.sendMessage({
        port: 510,
        command: "ALARM",
        payload: {
          level: "CRITICAL",
          source: "machine1",
          time: alarmTime,
          frame: frameNumber
        }
      });
    } else {
      // Reset alarm bit
      alarmStream.buffer[0] = 0x00;
    }
  });
};

Next Steps

Continue exploring the DragonFire Semantic Network:

DragonFire Developer Portal

Semantic Network

Related Documentation