Battery Life Optimization for AI-Powered Mobile Apps

import tensorflow as tf
import tensorflow_lite as tflite

def quantize_model(model_path, output_path):
    # Convert and quantize a TensorFlow model for mobile deployment
    
    # Load the saved model
    converter = tf.lite.TFLiteConverter.from_saved_model(model_path)
    
    # Enable quantization optimizations
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    
    # Use integer quantization for maximum efficiency
    converter.target_spec.supported_types = [tf.int8]
    
    # Representative dataset for calibration
    def representative_dataset():
        for _ in range(100):
            # Use actual data samples for better calibration
            yield [tf.random.normal((1, 224, 224, 3), dtype=tf.float32)]
    
    converter.representative_dataset = representative_dataset
    converter.inference_input_type = tf.uint8
    converter.inference_output_type = tf.uint8
    
    # Convert the model
    quantized_model = converter.convert()
    
    # Save the quantized model
    with open(output_path, 'wb') as f:
        f.write(quantized_model)
    
    return quantized_model

# Example usage
quantized_model = quantize_model(
    model_path='./models/image_classifier',
    output_path='./models/image_classifier_quantized.tflite'
)

# Performance comparison
def compare_model_performance(original_path, quantized_path):
    # Compare original vs quantized model performance
    
    # Load models
    original_interpreter = tf.lite.Interpreter(model_path=original_path)
    quantized_interpreter = tf.lite.Interpreter(model_path=quantized_path)
    
    original_interpreter.allocate_tensors()
    quantized_interpreter.allocate_tensors()
    
    # Get model sizes
    import os
    original_size = os.path.getsize(original_path) / (1024 * 1024)  # MB
    quantized_size = os.path.getsize(quantized_path) / (1024 * 1024)  # MB
    
    print(f"Original model size: {original_size:.2f} MB")
    print(f"Quantized model size: {quantized_size:.2f} MB")
    print(f"Size reduction: {((original_size - quantized_size) / original_size) * 100:.1f}%")
    
    return {
        'original_size_mb': original_size,
        'quantized_size_mb': quantized_size,
        'size_reduction_percent': ((original_size - quantized_size) / original_size) * 100
    }

import tensorflow_model_optimization as tfmot

def create_pruned_model(base_model, target_sparsity=0.5):
    # Create a pruned version of the model
    
    # Define pruning parameters
    pruning_params = {
        'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(
            initial_sparsity=0.0,
            final_sparsity=target_sparsity,
            begin_step=0,
            end_step=1000
        )
    }
    
    # Apply pruning to the model
    model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(
        base_model, **pruning_params
    )
    
    # Compile the pruned model
    model_for_pruning.compile(
        optimizer='adam',
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy']
    )
    
    return model_for_pruning

def fine_tune_pruned_model(pruned_model, train_data, validation_data):
    # Fine-tune the pruned model
    
    # Add pruning callbacks
    callbacks = [
        tfmot.sparsity.keras.UpdatePruningStep(),
        tfmot.sparsity.keras.PruningSummaries(log_dir='./logs')
    ]
    
    # Train the pruned model
    history = pruned_model.fit(
        train_data,
        validation_data=validation_data,
        epochs=10,
        callbacks=callbacks
    )
    
    # Remove pruning wrappers for final model
    final_model = tfmot.sparsity.keras.strip_pruning(pruned_model)
    
    return final_model, history

class DistillationTrainer:
    def __init__(self, teacher_model, student_model, temperature=3.0, alpha=0.7):
        self.teacher_model = teacher_model
        self.student_model = student_model
        self.temperature = temperature
        self.alpha = alpha
    
    def distillation_loss(self, y_true, y_pred_student, y_pred_teacher):
        """Calculate distillation loss combining hard and soft targets"""
        
        # Hard target loss (student vs ground truth)
        hard_loss = tf.keras.losses.sparse_categorical_crossentropy(
            y_true, y_pred_student
        )
        
        # Soft target loss (student vs teacher)
        teacher_soft = tf.nn.softmax(y_pred_teacher / self.temperature)
        student_soft = tf.nn.softmax(y_pred_student / self.temperature)
        
        soft_loss = tf.keras.losses.categorical_crossentropy(
            teacher_soft, student_soft
        )
        
        # Combine losses
        total_loss = (
            self.alpha * soft_loss * (self.temperature ** 2) +
            (1 - self.alpha) * hard_loss
        )
        
        return total_loss
    
    def train_student(self, train_data, validation_data, epochs=20):
        """Train the student model using knowledge distillation"""
        
        # Freeze teacher model
        self.teacher_model.trainable = False
        
        # Custom training loop
        optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
        
        for epoch in range(epochs):
            epoch_loss = 0
            num_batches = 0
            
            for batch_x, batch_y in train_data:
                with tf.GradientTape() as tape:
                    # Get predictions from both models
                    teacher_pred = self.teacher_model(batch_x, training=False)
                    student_pred = self.student_model(batch_x, training=True)
                    
                    # Calculate distillation loss
                    loss = self.distillation_loss(
                        batch_y, student_pred, teacher_pred
                    )
                
                # Update student model
                gradients = tape.gradient(loss, self.student_model.trainable_variables)
                optimizer.apply_gradients(
                    zip(gradients, self.student_model.trainable_variables)
                )
                
                epoch_loss += loss
                num_batches += 1
            
            avg_loss = epoch_loss / num_batches
            print(f"Epoch {epoch + 1}/{epochs}, Loss: {avg_loss:.4f}")
        
        return self.student_model

class AdaptiveInferenceScheduler {
  constructor() {
    this.batteryLevel = 1.0;
    this.isCharging = false;
    this.performanceMode = 'balanced'; // 'power_save', 'balanced', 'performance'
    this.inferenceQueue = [];
    this.isProcessing = false;
    
    this.initializeBatteryMonitoring();
  }
  
  initializeBatteryMonitoring() {
    if ('getBattery' in navigator) {
      navigator.getBattery().then(battery => {
        this.batteryLevel = battery.level;
        this.isCharging = battery.charging;
        
        // Listen for battery changes
        battery.addEventListener('levelchange', () => {
          this.batteryLevel = battery.level;
          this.adjustPerformanceMode();
        });
        
        battery.addEventListener('chargingchange', () => {
          this.isCharging = battery.charging;
          this.adjustPerformanceMode();
        });
      });
    }
  }
  
  adjustPerformanceMode() {
    if (this.isCharging) {
      this.performanceMode = 'performance';
    } else if (this.batteryLevel < 0.2) {
      this.performanceMode = 'power_save';
    } else if (this.batteryLevel < 0.5) {
      this.performanceMode = 'balanced';
    } else {
      this.performanceMode = 'performance';
    }
    
    console.log(`Applied performance profile: ${this.performanceMode}`);
  }
  
  getInferenceConfig() {
    const configs = {
      power_save: {
        maxConcurrentInferences: 1,
        inferenceInterval: 2000, // ms
        useQuantizedModel: true,
        batchSize: 1,
        skipFrames: 3 // Process every 4th frame
      },
      balanced: {
        maxConcurrentInferences: 2,
        inferenceInterval: 1000,
        useQuantizedModel: true,
        batchSize: 2,
        skipFrames: 1 // Process every 2nd frame
      },
      performance: {
        maxConcurrentInferences: 4,
        inferenceInterval: 500,
        useQuantizedModel: false,
        batchSize: 4,
        skipFrames: 0 // Process all frames
      }
    };
    
    return configs[this.performanceMode];
  }
  
  async scheduleInference(inputData, priority = 'normal') {
    const config = this.getInferenceConfig();
    
    // Add to queue with priority
    this.inferenceQueue.push({
      data: inputData,
      priority,
      timestamp: Date.now()
    });
    
    // Sort by priority and timestamp
    this.inferenceQueue.sort((a, b) => {
      if (a.priority === 'high' && b.priority !== 'high') return -1;
      if (b.priority === 'high' && a.priority !== 'high') return 1;
      return a.timestamp - b.timestamp;
    });
    
    // Process queue if not already processing
    if (!this.isProcessing) {
      this.processInferenceQueue();
    }
  }
  
  async processInferenceQueue() {
    if (this.inferenceQueue.length === 0) {
      this.isProcessing = false;
      return;
    }
    
    this.isProcessing = true;
    const config = this.getInferenceConfig();
    
    // Process up to maxConcurrentInferences
    const batch = this.inferenceQueue.splice(0, config.maxConcurrentInferences);
    
    try {
      const promises = batch.map(item => this.runInference(item.data, config));
      await Promise.all(promises);
    } catch (error) {
      console.error('Inference batch failed:', error);
    }
    
    // Schedule next batch processing
    setTimeout(() => {
      this.processInferenceQueue();
    }, config.inferenceInterval);
  }
  
  async runInference(inputData, config) {
    // Select model based on configuration
    const modelPath = config.useQuantizedModel 
      ? './models/quantized_model.tflite'
      : './models/full_model.tflite';
    
    // Run inference with appropriate batch size
    return await this.executeModel(modelPath, inputData, config.batchSize);
  }
  
  async executeModel(modelPath, inputData, batchSize) {
    // Implementation depends on your ML framework
    // This is a placeholder for actual model execution
    return new Promise(resolve => {
      setTimeout(() => {
        resolve({ prediction: 'example_result' });
      }, 100);
    });
  }
}

class IntelligentCache {
  constructor(maxSize = 100) {
    this.cache = new Map();
    this.maxSize = maxSize;
    this.accessCount = new Map();
    this.lastAccess = new Map();
  }
  
  generateCacheKey(input) {
    // Create a hash of the input for caching
    if (typeof input === 'object') {
      return JSON.stringify(input);
    }
    return String(input);
  }
  
  get(input) {
    const key = this.generateCacheKey(input);
    
    if (this.cache.has(key)) {
      // Update access statistics
      this.accessCount.set(key, (this.accessCount.get(key) || 0) + 1);
      this.lastAccess.set(key, Date.now());
      
      return this.cache.get(key);
    }
    
    return null;
  }
  
  set(input, result) {
    const key = this.generateCacheKey(input);
    
    // If cache is full, remove least valuable item
    if (this.cache.size >= this.maxSize) {
      this.evictLeastValuable();
    }
    
    this.cache.set(key, result);
    this.accessCount.set(key, 1);
    this.lastAccess.set(key, Date.now());
  }
  
  evictLeastValuable() {
    let leastValuableKey = null;
    let lowestScore = Infinity;
    
    for (const [key] of this.cache) {
      const accessCount = this.accessCount.get(key) || 0;
      const timeSinceAccess = Date.now() - (this.lastAccess.get(key) || 0);
      
      // Score based on access frequency and recency
      const score = accessCount / (1 + timeSinceAccess / 1000);
      
      if (score < lowestScore) {
        lowestScore = score;
        leastValuableKey = key;
      }
    }
    
    if (leastValuableKey) {
      this.cache.delete(leastValuableKey);
      this.accessCount.delete(leastValuableKey);
      this.lastAccess.delete(leastValuableKey);
    }
  }
  
  // Precompute results for common inputs
  async precomputeCommonResults(commonInputs, model) {
    for (const input of commonInputs) {
      if (!this.get(input)) {
        try {
          const result = await model.predict(input);
          this.set(input, result);
        } catch (error) {
          console.error('Precomputation failed for input:', input, error);
        }
      }
    }
  }
  
  getStats() {
    return {
      cacheSize: this.cache.size,
      totalAccesses: Array.from(this.accessCount.values()).reduce((a, b) => a + b, 0),
      hitRate: this.calculateHitRate()
    };
  }
  
  calculateHitRate() {
    // This would need to be tracked during actual usage
    // Placeholder implementation
    return 0.75; // 75% hit rate
  }
}