Cloud AI Workflow:
User Input → Network Request → Cloud Processing → Network Response → Result
Typical latency: 100-500ms + processing time
Network dependency: Critical
Bandwidth usage: High
User experience: Noticeable delay

Edge AI Workflow:
User Input → Local Processing → Result
Typical latency: 1-50ms
Network dependency: None
Bandwidth usage: Zero
User experience: Instant

// Cloud AI Privacy Concerns
const cloudAnalysis = {
  userVoice: "uploaded to third-party servers",
  personalPhotos: "processed on remote infrastructure", 
  privateDocuments: "analyzed outside user control",
  behaviorPatterns: "stored in vendor databases",
  dataRetention: "subject to vendor policies"
};

// Edge AI Privacy Benefits
const edgeAnalysis = {
  userVoice: "processed locally, never leaves device",
  personalPhotos: "analyzed on-device with user control",
  privateDocuments: "processed privately with local models",
  behaviorPatterns: "learned locally, stay with user",
  dataRetention: "under complete user control"
};

Cloud AI Dependencies:
  - Internet connectivity
  - Remote server availability
  - Third-party service uptime
  - Network infrastructure
  - API rate limits
  - Service pricing changes

Edge AI Independence:
  - Works offline
  - No external dependencies
  - Consistent performance
  - User-controlled environment
  - No usage limits
  - Predictable costs

// Real-time video analysis example
class RealTimeVideoAnalysis {
  private model: EdgeModel;
  private videoStream: MediaStream;
  
  async processVideoFrame(frame: VideoFrame): Promise<Analysis> {
    // Process at 60fps without network latency
    const analysis = await this.model.analyze(frame);
    
    // Immediate response enables real-time applications:
    // - Live video effects and filters
    // - Real-time object detection and tracking
    // - Instant facial recognition and emotion detection
    // - Live augmented reality overlays
    
    return analysis;
  }
}

interface PrivacyFirstAI {
  // All processing happens locally
  processData(sensitiveData: UserData): Promise<Analysis>;
  
  // No data leaves the device
  storeModelWeights(): LocalStorage;
  
  // User controls all AI capabilities
  enableFeature(feature: AIFeature): UserControlledFeature;
  
  // Transparent operation
  explainProcessing(): ProcessingExplanation;
}

# Model selection criteria for edge deployment
class EdgeModelSelector:
    def evaluate_model(self, model, constraints):
        """Evaluate model suitability for edge deployment"""
        
        metrics = {
            'model_size': self.measure_size(model),           # < 100MB preferred
            'inference_speed': self.measure_speed(model),     # < 50ms target
            'memory_usage': self.measure_memory(model),       # < 500MB RAM
            'cpu_efficiency': self.measure_cpu_usage(model),  # < 50% CPU
            'battery_impact': self.measure_battery(model),    # Minimal drain
            'accuracy_retention': self.measure_accuracy(model) # > 90% of full model
        }
        
        return self.calculate_edge_suitability(metrics, constraints)
    
    def optimize_for_edge(self, model):
        """Optimize model for edge deployment"""
        
        optimizations = [
            self.quantize_weights(model),      # 8-bit or 16-bit quantization
            self.prune_redundant_weights(model),  # Remove unnecessary parameters
            self.optimize_operations(model),   # Fuse operations for efficiency
            self.convert_to_edge_format(model) # TensorFlow Lite, CoreML, ONNX
        ]
        
        return self.apply_optimizations(model, optimizations)

// Advanced model compression for edge deployment
class ModelCompression {
  // Quantization: Reduce precision while maintaining accuracy
  quantizeModel(model, precision = 'int8') {
    const quantizationConfig = {
      representativeDataset: this.getRepresentativeData(),
      optimizations: ['DEFAULT'],
      targetSpec: {
        supportedTypes: [precision]
      }
    };
    
    return tf.lite.TFLiteConverter.fromSavedModel(model)
      .convert(quantizationConfig);
  }
  
  // Pruning: Remove redundant model parameters
  pruneModel(model, sparsity = 0.5) {
    return tf.model.prune(model, {
      sparsity: sparsity,
      structuredSparsity: true,
      blockSize: [1, 1]
    });
  }
  
  // Knowledge distillation: Train smaller model to mimic larger one
  async distillModel(teacherModel, studentArchitecture) {
    const distillationTrainer = new KnowledgeDistillation({
      teacher: teacherModel,
      student: studentArchitecture,
      temperature: 3.0,
      alpha: 0.7
    });
    
    return await distillationTrainer.train();
  }
}

import CoreML
import Vision

class iOSEdgeAI {
    private var model: VNCoreMLModel?
    
    func loadModel() {
        guard let modelURL = Bundle.main.url(forResource: "EdgeModel", withExtension: "mlmodel"),
              let coreMLModel = try? MLModel(contentsOf: modelURL),
              let visionModel = try? VNCoreMLModel(for: coreMLModel) else {
            return
        }
        
        self.model = visionModel
    }
    
    func processImage(_ image: UIImage, completion: @escaping (Analysis) -> Void) {
        guard let model = model else { return }
        
        let request = VNCoreMLRequest(model: model) { request, error in
            guard let results = request.results as? [VNClassificationObservation] else {
                return
            }
            
            let analysis = Analysis(
                predictions: results.map { Prediction(label: $0.identifier, confidence: $0.confidence) },
                processingTime: Date().timeIntervalSince(startTime),
                modelVersion: "1.0"
            )
            
            DispatchQueue.main.async {
                completion(analysis)
            }
        }
        
        // Process on background queue for performance
        DispatchQueue.global(qos: .userInitiated).async {
            let handler = VNImageRequestHandler(cgImage: image.cgImage!, options: [:])
            try? handler.perform([request])
        }
    }
}

import org.tensorflow.lite.Interpreter
import org.tensorflow.lite.gpu.GpuDelegate

class AndroidEdgeAI {
    private var interpreter: Interpreter? = null
    private val gpuDelegate = GpuDelegate()
    
    fun loadModel(context: Context) {
        try {
            val modelFile = loadModelFile(context, "edge_model.tflite")
            val options = Interpreter.Options().apply {
                // Use GPU acceleration when available
                addDelegate(gpuDelegate)
                // Optimize for low latency
                setNumThreads(4)
                setUseXNNPACK(true)
            }
            
            interpreter = Interpreter(modelFile, options)
        } catch (e: Exception) {
            Log.e("EdgeAI", "Failed to load model", e)
        }
    }
    
    fun processImage(bitmap: Bitmap): Analysis {
        val interpreter = this.interpreter ?: return Analysis.empty()
        
        // Preprocess image
        val inputBuffer = preprocessImage(bitmap)
        val outputBuffer = Array(1) { FloatArray(NUM_CLASSES) }
        
        // Run inference
        val startTime = System.currentTimeMillis()
        interpreter.run(inputBuffer, outputBuffer)
        val inferenceTime = System.currentTimeMillis() - startTime
        
        // Postprocess results
        return Analysis(
            predictions = postprocessOutput(outputBuffer[0]),
            processingTime = inferenceTime,
            modelVersion = getModelVersion()
        )
    }
    
    private fun preprocessImage(bitmap: Bitmap): ByteBuffer {
        val inputSize = 224
        val pixelSize = 3 // RGB
        val bufferSize = 1 * inputSize * inputSize * pixelSize * 4 // 4 bytes per float
        
        val buffer = ByteBuffer.allocateDirect(bufferSize).apply {
            order(ByteOrder.nativeOrder())
        }
        
        val scaledBitmap = Bitmap.createScaledBitmap(bitmap, inputSize, inputSize, true)
        val pixels = IntArray(inputSize * inputSize)
        scaledBitmap.getPixels(pixels, 0, inputSize, 0, 0, inputSize, inputSize)
        
        for (pixel in pixels) {
            buffer.putFloat(((pixel shr 16) and 0xFF) / 255.0f) // R
            buffer.putFloat(((pixel shr 8) and 0xFF) / 255.0f)  // G
            buffer.putFloat((pixel and 0xFF) / 255.0f)          // B
        }
        
        return buffer
    }
}

class WebEdgeAI {
  constructor() {
    this.model = null;
    this.initialized = false;
  }
  
  async initialize() {
    try {
      // Load model with WebGL acceleration
      await tf.setBackend('webgl');
      
      // Load quantized model for better performance
      this.model = await tf.loadLayersModel('/models/edge-model.json');
      
      // Warm up the model
      await this.warmUpModel();
      
      this.initialized = true;
    } catch (error) {
      console.error('Failed to initialize Edge AI:', error);
      
      // Fallback to CPU backend
      await tf.setBackend('cpu');
      this.model = await tf.loadLayersModel('/models/edge-model-cpu.json');
      this.initialized = true;
    }
  }
  
  async processImage(imageElement) {
    if (!this.initialized || !this.model) {
      throw new Error('Edge AI not initialized');
    }
    
    // Preprocess image
    const tensor = tf.browser.fromPixels(imageElement)
      .resizeBilinear([224, 224])
      .expandDims(0)
      .div(255.0);
    
    try {
      // Run inference
      const startTime = performance.now();
      const predictions = await this.model.predict(tensor);
      const inferenceTime = performance.now() - startTime;
      
      // Get predictions
      const results = await predictions.data();
      
      return {
        predictions: this.postprocessResults(results),
        processingTime: inferenceTime,
        backend: tf.getBackend()
      };
    } finally {
      // Clean up tensors to prevent memory leaks
      tensor.dispose();
    }
  }
  
  async warmUpModel() {
    // Create dummy input to warm up the model
    const dummyInput = tf.zeros([1, 224, 224, 3]);
    
    try {
      await this.model.predict(dummyInput);
    } finally {
      dummyInput.dispose();
    }
  }
  
  postprocessResults(rawResults) {
    const classes = this.getClassLabels();
    
    return Array.from(rawResults)
      .map((confidence, index) => ({
        label: classes[index],
        confidence: confidence
      }))
      .sort((a, b) => b.confidence - a.confidence)
      .slice(0, 5); // Top 5 predictions
  }
}

// Rust-based WASM module for high-performance edge AI
use wasm_bindgen::prelude::*;
use ndarray::{Array, Array2, Array4};

#[wasm_bindgen]
pub struct EdgeAIEngine {
    model_weights: Vec<f32>,
    input_shape: Vec<usize>,
    output_shape: Vec<usize>,
}

#[wasm_bindgen]
impl EdgeAIEngine {
    #[wasm_bindgen(constructor)]
    pub fn new(weights: &[f32], input_shape: &[usize], output_shape: &[usize]) -> EdgeAIEngine {
        EdgeAIEngine {
            model_weights: weights.to_vec(),
            input_shape: input_shape.to_vec(),
            output_shape: output_shape.to_vec(),
        }
    }
    
    #[wasm_bindgen]
    pub fn infer(&self, input_data: &[f32]) -> Vec<f32> {
        // High-performance inference implementation
        let input = Array::from_shape_vec(
            self.input_shape.clone(), 
            input_data.to_vec()
        ).unwrap();
        
        // Optimized matrix operations
        let result = self.forward_pass(input);
        
        result.into_raw_vec()
    }
    
    fn forward_pass(&self, input: Array4<f32>) -> Array2<f32> {
        // Implement optimized forward pass
        // Using SIMD instructions when available
        // Optimized for specific model architecture
        
        // ... implementation details ...
        Array2::zeros((1, self.output_shape[1]))
    }
}

// JavaScript integration

// JavaScript wrapper for WASM AI engine
class WASMEdgeAI {
  constructor() {
    this.engine = null;
    this.wasmModule = null;
  }
  
  async initialize(modelPath) {
    // Load WASM module
    this.wasmModule = await import('./edge_ai_engine.js');
    await this.wasmModule.default();
    
    // Load model weights
    const modelData = await this.loadModelData(modelPath);
    
    // Initialize engine
    this.engine = new this.wasmModule.EdgeAIEngine(
      modelData.weights,
      modelData.inputShape,
      modelData.outputShape
    );
  }
  
  async processData(inputData) {
    if (!this.engine) {
      throw new Error('WASM Engine not initialized');
    }
    
    const startTime = performance.now();
    const results = this.engine.infer(inputData);
    const processingTime = performance.now() - startTime;
    
    return {
      results: Array.from(results),
      processingTime: processingTime,
      backend: 'wasm'
    };
  }
}

class PrivatePhotoAnalyzer {
    private let modelManager = EdgeModelManager()
    
    func analyzePhoto(_ image: UIImage) async -> PhotoAnalysis {
        // All processing happens on-device
        let analysis = await modelManager.analyze(image)
        
        return PhotoAnalysis(
            objects: analysis.detectedObjects,
            scenes: analysis.recognizedScenes,
            faces: analysis.faceDetection,
            text: analysis.textRecognition,
            // Sensitive data never leaves device
            metadata: PhotoMetadata(
                processedLocally: true,
                timestamp: Date(),
                modelVersion: modelManager.version
            )
        )
    }
    
    func createSmartAlbums() async {
        // Organize photos using on-device AI
        let photos = await PhotoLibrary.getAllPhotos()
        
        for photo in photos {
            let analysis = await analyzePhoto(photo.image)
            await PhotoLibrary.addToSmartAlbum(photo, basedOn: analysis)
        }
        
        // User's photo organization stays private
    }
}

class OfflineLanguageProcessor {
  constructor() {
    this.translationModel = null;
    this.sentimentModel = null;
    this.languageDetector = null;
  }
  
  async initialize() {
    // Load compressed language models
    const modelPromises = [
      this.loadTranslationModel(),
      this.loadSentimentModel(),
      this.loadLanguageDetector()
    ];
    
    [this.translationModel, this.sentimentModel, this.languageDetector] = 
      await Promise.all(modelPromises);
  }
  
  async processText(text) {
    // Detect language without sending data to servers
    const language = await this.languageDetector.detect(text);
    
    // Analyze sentiment locally
    const sentiment = await this.sentimentModel.analyze(text, language);
    
    // Translate if needed (all on-device)
    const translation = language !== 'en' ? 
      await this.translationModel.translate(text, language, 'en') : 
      null;
    
    return {
      originalText: text,
      detectedLanguage: language,
      sentiment: sentiment,
      translation: translation,
      processedLocally: true,
      timestamp: new Date()
    };
  }
  
  async enableRealTimeTranslation(sourceLanguage, targetLanguage) {
    // Real-time translation for conversations
    return new RealtimeTranslator({
      source: sourceLanguage,
      target: targetLanguage,
      model: this.translationModel,
      latency: 'ultra-low' // < 100ms response time
    });
  }
}

class LocalCodeIntelligence {
  private codeAnalysisModel: EdgeModel;
  private securityScanner: SecurityModel;
  private performanceAnalyzer: PerformanceModel;
  
  async analyzeCodebase(projectPath: string): Promise<CodebaseAnalysis> {
    const files = await this.scanCodeFiles(projectPath);
    const analyses: FileAnalysis[] = [];
    
    for (const file of files) {
      const analysis = await this.analyzeFile(file);
      analyses.push(analysis);
    }
    
    return {
      files: analyses,
      overallHealth: this.calculateHealthScore(analyses),
      suggestions: this.generateSuggestions(analyses),
      securityIssues: this.identifySecurityIssues(analyses),
      performanceOpportunities: this.findPerformanceIssues(analyses),
      processedLocally: true
    };
  }
  
  private async analyzeFile(file: CodeFile): Promise<FileAnalysis> {
    const [
      codeQuality,
      securityScan,
      performanceAnalysis
    ] = await Promise.all([
      this.codeAnalysisModel.analyze(file.content),
      this.securityScanner.scan(file.content),
      this.performanceAnalyzer.analyze(file.content)
    ]);
    
    return {
      fileName: file.name,
      quality: codeQuality,
      security: securityScan,
      performance: performanceAnalysis,
      suggestions: this.generateFileSuggestions(codeQuality, securityScan, performanceAnalysis)
    };
  }
  
  async enableRealtimeAnalysis(editor: CodeEditor): Promise<void> {
    editor.onTextChange(async (change) => {
      // Analyze code as it's being written
      const analysis = await this.analyzeCodeBlock(change.text);
      
      // Provide immediate feedback
      editor.showInlineAnnotations(analysis.suggestions);
      editor.highlightIssues(analysis.issues);
    });
  }
}

// C++ implementation for embedded edge AI
class EmbeddedEdgeAI {
private:
    std::unique_ptr<TensorFlowLiteModel> model;
    std::vector<float> inputBuffer;
    std::vector<float> outputBuffer;
    
public:
    bool initialize(const char* modelPath) {
        // Load optimized model for embedded hardware
        model = std::make_unique<TensorFlowLiteModel>();
        
        if (!model->loadFromFile(modelPath)) {
            return false;
        }
        
        // Allocate buffers
        inputBuffer.resize(model->getInputSize());
        outputBuffer.resize(model->getOutputSize());
        
        return true;
    }
    
    SensorAnalysis analyzeSensorData(const SensorReading& reading) {
        // Preprocess sensor data
        preprocessSensorData(reading, inputBuffer.data());
        
        // Run inference on embedded hardware
        auto startTime = std::chrono::high_resolution_clock::now();
        model->runInference(inputBuffer.data(), outputBuffer.data());
        auto endTime = std::chrono::high_resolution_clock::now();
        
        auto duration = std::chrono::duration_cast<std::chrono::microseconds>(
            endTime - startTime
        ).count();
        
        // Postprocess results
        SensorAnalysis analysis;
        analysis.anomalyScore = outputBuffer[0];
        analysis.predictedValue = outputBuffer[1];
        analysis.confidence = outputBuffer[2];
        analysis.inferenceTime = duration;
        
        return analysis;
    }
    
    void enableContinuousMonitoring(SensorManager& sensors) {
        sensors.setCallback([this](const SensorReading& reading) {
            auto analysis = analyzeSensorData(reading);
            
            if (analysis.anomalyScore > ANOMALY_THRESHOLD) {
                triggerAlert(analysis);
            }
            
            updatePredictiveModel(analysis);
        });
    }
};

class EdgeOptimizedModel:
    def __init__(self):
        self.optimization_strategies = [
            'depth_wise_separable_convolutions',
            'mobile_net_blocks',
            'efficient_net_scaling',
            'squeeze_and_excitation',
            'lightweight_attention'
        ]
    
    def design_edge_model(self, task_requirements):
        """Design model optimized for edge deployment"""
        
        model_config = {
            'architecture': self.select_efficient_architecture(task_requirements),
            'width_multiplier': self.calculate_optimal_width(task_requirements),
            'depth_multiplier': self.calculate_optimal_depth(task_requirements),
            'resolution': self.optimize_input_resolution(task_requirements),
            'activation': 'relu6',  # Hardware-friendly activation
            'normalization': 'batch_norm',  # Efficient normalization
        }
        
        return self.build_model(model_config)
    
    def apply_neural_architecture_search(self, constraints):
        """Use NAS to find optimal edge architecture"""
        
        search_space = {
            'blocks': ['mobilenet_v3', 'efficientnet', 'shufflenet'],
            'channels': [16, 24, 32, 48, 64, 96, 128],
            'layers': [1, 2, 3, 4, 5, 6],
            'kernel_sizes': [3, 5, 7],
            'skip_connections': [True, False]
        }
        
        return self.nas_search(search_space, constraints)

class HardwareOptimizer {
  constructor() {
    this.capabilities = null;
  }
  
  async detectCapabilities() {
    this.capabilities = {
      // WebGL capabilities
      webgl: await this.detectWebGLFeatures(),
      
      // WebGPU support
      webgpu: await this.detectWebGPUSupport(),
      
      // CPU features
      cpu: {
        cores: navigator.hardwareConcurrency,
        simd: await this.detectSIMDSupport(),
        threads: await this.detectWebWorkersSupport()
      },
      
      // Memory constraints
      memory: {
        total: await this.estimateAvailableMemory(),
        heap: performance.memory?.totalJSHeapSize
      }
    };
  }
  
  optimizeForPlatform(model, targetPlatform) {
    const optimizations = [];
    
    if (this.capabilities.webgpu) {
      optimizations.push(this.enableWebGPUAcceleration(model));
    } else if (this.capabilities.webgl) {
      optimizations.push(this.enableWebGLAcceleration(model));
    }
    
    if (this.capabilities.cpu.simd) {
      optimizations.push(this.enableSIMDOptimizations(model));
    }
    
    if (this.capabilities.cpu.cores > 4) {
      optimizations.push(this.enableMultiThreading(model));
    }
    
    return this.applyOptimizations(model, optimizations);
  }
  
  async benchmarkModel(model) {
    const benchmarks = [];
    
    // Warmup runs
    for (let i = 0; i < 5; i++) {
      await model.predict(this.getDummyInput());
    }
    
    // Actual benchmarks
    for (let i = 0; i < 100; i++) {
      const startTime = performance.now();
      await model.predict(this.getDummyInput());
      const endTime = performance.now();
      
      benchmarks.push(endTime - startTime);
    }
    
    return {
      mean: benchmarks.reduce((a, b) => a + b) / benchmarks.length,
      median: this.calculateMedian(benchmarks),
      p95: this.calculatePercentile(benchmarks, 95),
      p99: this.calculatePercentile(benchmarks, 99)
    };
  }
}

class EdgeMemoryManager {
  private memoryPool: Float32Array[];
  private availableBuffers: Set<Float32Array>;
  private usedBuffers: Map<string, Float32Array>;
  
  constructor(maxMemoryMB: number = 100) {
    this.initializeMemoryPool(maxMemoryMB);
  }
  
  private initializeMemoryPool(maxMemoryMB: number): void {
    const totalFloats = (maxMemoryMB * 1024 * 1024) / 4; // 4 bytes per float
    const bufferSizes = [1024, 4096, 16384, 65536, 262144]; // Common tensor sizes
    
    this.memoryPool = [];
    this.availableBuffers = new Set();
    
    bufferSizes.forEach(size => {
      const numBuffers = Math.floor(totalFloats / (size * bufferSizes.length));
      
      for (let i = 0; i < numBuffers; i++) {
        const buffer = new Float32Array(size);
        this.memoryPool.push(buffer);
        this.availableBuffers.add(buffer);
      }
    });
  }
  
  allocateBuffer(requiredSize: number, id: string): Float32Array {
    // Find suitable buffer from pool
    for (const buffer of this.availableBuffers) {
      if (buffer.length >= requiredSize) {
        this.availableBuffers.delete(buffer);
        this.usedBuffers.set(id, buffer);
        return buffer.subarray(0, requiredSize);
      }
    }
    
    // Fallback: create new buffer (not ideal)
    console.warn('Memory pool exhausted, creating new buffer');
    const buffer = new Float32Array(requiredSize);
    this.usedBuffers.set(id, buffer);
    return buffer;
  }
  
  releaseBuffer(id: string): void {
    const buffer = this.usedBuffers.get(id);
    if (buffer) {
      this.usedBuffers.delete(id);
      this.availableBuffers.add(buffer);
    }
  }
  
  getMemoryUsage(): MemoryStats {
    const totalBuffers = this.memoryPool.length;
    const usedBuffers = this.usedBuffers.size;
    const availableBuffers = this.availableBuffers.size;
    
    return {
      totalBuffers,
      usedBuffers,
      availableBuffers,
      utilizationPercentage: (usedBuffers / totalBuffers) * 100
    };
  }
}

class UltraLightweightModel:
    def __init__(self, target_size_mb=5):
        self.target_size = target_size_mb * 1024 * 1024  # Convert to bytes
        self.compression_strategies = []
    
    def aggressive_quantization(self, model):
        """Apply aggressive quantization techniques"""
        
        # Dynamic quantization
        quantized_model = torch.quantization.quantize_dynamic(
            model, {torch.nn.Linear, torch.nn.Conv2d}, dtype=torch.qint8
        )
        
        # Post-training quantization
        model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
        torch.quantization.prepare(model, inplace=True)
        
        # Calibrate with representative data
        with torch.no_grad():
            for data in self.get_calibration_data():
                model(data)
        
        # Convert to quantized model
        torch.quantization.convert(model, inplace=True)
        
        return quantized_model
    
    def structured_pruning(self, model, sparsity=0.7):
        """Remove entire channels/filters for better compression"""
        
        import torch.nn.utils.prune as prune
        
        # Identify pruning targets
        modules_to_prune = []
        for name, module in model.named_modules():
            if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
                modules_to_prune.append((module, 'weight'))
        
        # Apply structured pruning
        prune.global_unstructured(
            modules_to_prune,
            pruning_method=prune.L1Unstructured,
            amount=sparsity
        )
        
        # Remove pruning reparameterization
        for module, _ in modules_to_prune:
            prune.remove(module, 'weight')
        
        return model
    
    def neural_architecture_distillation(self, teacher_model, student_config):
        """Train ultra-compact student model"""
        
        student_model = self.build_student_model(student_config)
        
        # Distillation loss
        def distillation_loss(student_output, teacher_output, labels, temperature=3.0, alpha=0.5):
            soft_targets = F.softmax(teacher_output / temperature, dim=1)
            soft_student = F.log_softmax(student_output / temperature, dim=1)
            
            distillation_loss = F.kl_div(soft_student, soft_targets, reduction='batchmean')
            student_loss = F.cross_entropy(student_output, labels)
            
            return alpha * distillation_loss + (1 - alpha) * student_loss
        
        # Training loop with knowledge distillation
        return self.train_with_distillation(student_model, teacher_model, distillation_loss)

// Optimized C++ inference engine
class FastInferenceEngine {
private:
    std::vector<float> weights;
    std::vector<int> layer_configs;
    float* weight_ptr;
    
    // Pre-allocated buffers
    alignas(32) float input_buffer[MAX_INPUT_SIZE];
    alignas(32) float output_buffer[MAX_OUTPUT_SIZE];
    alignas(32) float intermediate_buffer[MAX_INTERMEDIATE_SIZE];
    
public:
    void optimized_conv2d(
        const float* input, 
        const float* weights, 
        float* output,
        int channels, int height, int width,
        int filters, int kernel_size
    ) {
        // SIMD-optimized convolution
        #pragma omp parallel for
        for (int f = 0; f < filters; f++) {
            for (int h = 0; h < height - kernel_size + 1; h++) {
                for (int w = 0; w < width - kernel_size + 1; w++) {
                    __m256 sum = _mm256_setzero_ps();
                    
                    for (int c = 0; c < channels; c++) {
                        for (int kh = 0; kh < kernel_size; kh++) {
                            for (int kw = 0; kw < kernel_size; kw += 8) {
                                __m256 input_vec = _mm256_load_ps(
                                    &input[c * height * width + 
                                          (h + kh) * width + (w + kw)]
                                );
                                __m256 weight_vec = _mm256_load_ps(
                                    &weights[f * channels * kernel_size * kernel_size +
                                            c * kernel_size * kernel_size +
                                            kh * kernel_size + kw]
                                );
                                sum = _mm256_fmadd_ps(input_vec, weight_vec, sum);
                            }
                        }
                    }
                    
                    // Horizontal sum and store
                    float result[8];
                    _mm256_store_ps(result, sum);
                    output[f * (height - kernel_size + 1) * (width - kernel_size + 1) +
                           h * (width - kernel_size + 1) + w] = 
                        result[0] + result[1] + result[2] + result[3] +
                        result[4] + result[5] + result[6] + result[7];
                }
            }
        }
    }
    
    void run_inference(const float* input, float* output) {
        // Copy input to aligned buffer
        std::memcpy(input_buffer, input, input_size * sizeof(float));
        
        float* current_input = input_buffer;
        float* current_output = intermediate_buffer;
        
        // Process layers with optimized kernels
        for (const auto& layer : layers) {
            switch (layer.type) {
                case LayerType::CONV2D:
                    optimized_conv2d(
                        current_input, 
                        &weights[layer.weight_offset],
                        current_output,
                        layer.channels, layer.height, layer.width,
                        layer.filters, layer.kernel_size
                    );
                    break;
                    
                case LayerType::RELU:
                    optimized_relu(current_input, current_output, layer.size);
                    break;
                    
                case LayerType::POOL:
                    optimized_pooling(current_input, current_output, layer);
                    break;
            }
            
            std::swap(current_input, current_output);
        }
        
        // Copy final output
        std::memcpy(output, current_input, output_size * sizeof(float));
    }
};

class UnifiedEdgeDeployment {
  private deploymentTargets: Map<Platform, DeploymentConfig>;
  
  constructor() {
    this.deploymentTargets = new Map([
      ['ios', new iOSDeploymentConfig()],
      ['android', new AndroidDeploymentConfig()],
      ['web', new WebDeploymentConfig()],
      ['embedded', new EmbeddedDeploymentConfig()]
    ]);
  }
  
  async deployModel(model: Model, targets: Platform[]): Promise<DeploymentResult[]> {
    const deploymentPromises = targets.map(async target => {
      const config = this.deploymentTargets.get(target);
      if (!config) {
        throw new Error(`Unsupported platform: ${target}`);
      }
      
      // Platform-specific optimization
      const optimizedModel = await config.optimizeModel(model);
      
      // Convert to platform format
      const platformModel = await config.convertModel(optimizedModel);
      
      // Validate deployment
      const validation = await config.validateModel(platformModel);
      
      return {
        platform: target,
        model: platformModel,
        validation: validation,
        size: await config.getModelSize(platformModel),
        expectedPerformance: await config.benchmarkModel(platformModel)
      };
    });
    
    return Promise.all(deploymentPromises);
  }
  
  generateDeploymentPackage(deploymentResults: DeploymentResult[]): DeploymentPackage {
    return {
      models: deploymentResults.reduce((acc, result) => {
        acc[result.platform] = result.model;
        return acc;
      }, {} as Record<Platform, any>),
      
      documentation: this.generateDocumentation(deploymentResults),
      
      integrationGuides: this.generateIntegrationGuides(deploymentResults),
      
      performanceMetrics: this.consolidateMetrics(deploymentResults)
    };
  }
}

interface NextGenEdgeHardware {
  // Neural Processing Units
  npu: {
    ops_per_second: number; // TOPS (Trillions of Operations Per Second)
    power_efficiency: number; // TOPS/Watt
    specialized_operations: string[]; // Transformer, CNN, RNN optimizations
  };
  
  // In-Memory Computing
  in_memory_compute: {
    storage_class_memory: boolean;
    compute_near_data: boolean;
    reduced_data_movement: boolean;
  };
  
  // Photonic Computing
  photonic_acceleration: {
    optical_matrix_multiplication: boolean;
    ultra_low_latency: boolean; // Sub-nanosecond operations
    massive_parallelism: boolean;
  };
  
  // Neuromorphic Chips
  neuromorphic: {
    event_driven_processing: boolean;
    ultra_low_power: boolean; // Microwatts
    adaptive_learning: boolean;
  };
}

class NextGenEdgeFramework:
    def __init__(self):
        self.auto_optimization = AutoOptimizer()
        self.federated_learning = FederatedLearningManager()
        self.adaptive_inference = AdaptiveInferenceEngine()
    
    async def auto_optimize_for_device(self, model, device_specs):
        """Automatically optimize model for specific device"""
        
        optimization_plan = await self.auto_optimization.analyze_device(device_specs)
        
        optimizations = [
            self.auto_quantization(model, device_specs.precision_support),
            self.auto_pruning(model, device_specs.memory_constraints),
            self.auto_architecture_search(model, device_specs.compute_capabilities),
            self.auto_compiler_optimization(model, device_specs.hardware_features)
        ]
        
        optimized_model = await self.apply_optimizations(model, optimizations)
        
        # Validate optimization didn't break accuracy
        validation_result = await self.validate_optimization(optimized_model, model)
        
        return {
            'model': optimized_model,
            'optimization_report': optimization_plan,
            'validation': validation_result,
            'expected_performance': await self.benchmark_on_device(optimized_model, device_specs)
        }
    
    async def enable_federated_learning(self, model, privacy_constraints):
        """Enable privacy-preserving distributed learning"""
        
        return await self.federated_learning.setup_federation({
            'model': model,
            'privacy_budget': privacy_constraints.differential_privacy_budget,
            'secure_aggregation': privacy_constraints.require_secure_aggregation,
            'homomorphic_encryption': privacy_constraints.require_homomorphic_encryption
        })

class IntelligentEdgeOrchestrator {
  private edgeNodes: EdgeNode[];
  private loadBalancer: EdgeLoadBalancer;
  private modelDistributor: ModelDistributor;
  
  async optimizeComputeDistribution(tasks: AITask[]): Promise<OptimizationPlan> {
    const nodeCapabilities = await this.assessNodeCapabilities();
    const taskRequirements = this.analyzeTaskRequirements(tasks);
    
    return this.createOptimalDistribution(nodeCapabilities, taskRequirements);
  }
  
  async enableCollaborativeInference(model: Model): Promise<CollaborativeNetwork> {
    // Split model across multiple edge devices
    const modelPartitions = await this.partitionModel(model);
    
    // Create inference pipeline
    const pipeline = new DistributedInferencePipeline({
      partitions: modelPartitions,
      nodes: this.edgeNodes,
      failoverStrategy: 'graceful_degradation',
      latencyTarget: 50 // milliseconds
    });
    
    return pipeline;
  }
  
  async adaptToNetworkConditions(): Promise<void> {
    const networkStatus = await this.monitorNetworkHealth();
    
    if (networkStatus.congestion > 0.8) {
      // Switch to local-only inference
      await this.enableLocalOnlyMode();
    } else if (networkStatus.bandwidth > this.getThreshold('high_bandwidth')) {
      // Enable collaborative inference
      await this.enableCollaborativeMode();
    }
  }
}