Edge AI: Intelligent Computing at the Source 2026

class EdgeAILayers:
    """
    Edge AI deployment layers.
    """
    
    def layer_definition(self):
        """
        Different edge tiers.
        """
        return {
            'device': {
                'examples': 'Smartphone, IoT sensor',
                'compute': 'Limited (1-10 TOPS)',
                'model': 'Small (1-100 MB)'
            },
            'gateway': {
                'examples': 'Edge server, NVidia Jetson',
                'compute': 'Medium (10-100 TOPS)',
                'model': 'Medium (100MB-1GB)'
            },
            'on_prem': {
                'examples': 'Edge data center',
                'compute': 'High (100+ TOPS)',
                'model': 'Large (1GB+)'
            }
        }

import torch
import torch.quantization

class ModelQuantization:
    """
    Reduce model precision for edge deployment.
    """
    
    def dynamic_quantization(self, model):
        """
        Dynamic quantization (Post-Training).
        """
        # Convert to INT8 dynamically
        quantized = torch.quantization.quantize_dynamic(
            model,
            {torch.nn.Linear, torch.nn.LSTM},
            dtype=torch.qint8
        )
        
        return quantized
    
    def static_quantization(self, model, dataloader):
        """
        Static quantization with calibration.
        """
        # Prepare model
        model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
        torch.quantization.prepare(model, inplace=True)
        
        # Calibrate
        with torch.no_grad():
            for data, _ in dataloader:
                model(data)
        
        # Convert
        quantized = torch.quantization.convert(model, inplace=False)
        
        return quantized
    
    def quantization_aware_training(self, model):
        """
        QAT: Train with quantization in mind.
        """
        # Prepare for QAT
        model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')
        torch.quantization.prepare_qat(model, inplace=True)
        
        # Train normally
        # ...
        
        # Convert
        quantized = torch.quantization.convert(model, inplace=False)
        
        return quantized
    
    def benchmark(self):
        """
        Quantization impact.
        """
        return {
            'fp32_size': '100%',
            'int8_size': '25%',
            'int4_size': '6.25%',
            'speedup': '2-4x',
            'accuracy_loss': '<1% with proper quantization'
        }

class ModelPruning:
    """
    Remove unnecessary connections.
    """
    
    def magnitude_pruning(self, model, sparsity=0.5):
        """
        Remove low-magnitude weights.
        """
        for name, param in model.named_parameters():
            if 'weight' in name:
                # Calculate threshold
                threshold = torch.quantile(
                    torch.abs(param.data),
                    sparsity
                )
                
                # Zero out below threshold
                mask = torch.abs(param.data) > threshold
                param.data *= mask.float()
        
        return model
    
    def structured_pruning(self, model):
        """
        Remove entire neurons/channels.
        """
        # Remove entire channels
        for module in model.modules():
            if isinstance(module, torch.nn.Conv2d):
                # Calculate channel importance (L1 norm)
                importance = module.weight.data.abs().mean(dim=(2, 3))
                
                # Keep top 50%
                keep = importance > torch.quantile(importance, 0.5)
                
                # Note: Requires actual channel removal implementation
                pass
        
        return model
    
    def results(self):
        """
        Pruning impact.
        """
        return {
            'sparsity': '50-90%',
            'speedup': '1.5-3x',
            'accuracy': 'Maintained with gradual pruning'
        }

class KnowledgeDistillation:
    """
    Train small model from large model.
    """
    
    def __init__(self, teacher_model, student_model):
        self.teacher = teacher_model
        self.student = student_model
        self.temperature = 4.0
        self.alpha = 0.7
    
    def distill(self, dataloader, optimizer):
        """
        Train student to mimic teacher.
        """
        for data, labels in dataloader:
            # Teacher predictions (soft)
            with torch.no_grad():
                teacher_logits = self.teacher(data)
                teacher_probs = F.softmax(teacher_logits / self.temperature, dim=-1)
            
            # Student predictions
            student_logits = self.student(data)
            student_log_probs = F.log_softmax(student_logits / self.temperature, dim=-1)
            
            # Distillation loss
            distill_loss = F.kl_div(
                student_log_probs,
                teacher_probs,
                reduction='batchmean'
            ) * (self.temperature ** 2)
            
            # Hard label loss
            hard_loss = F.cross_entropy(student_logits, labels)
            
            # Combined loss
            loss = self.alpha * distill_loss + (1 - self.alpha) * hard_loss
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        
        return self.student
    
    def results(self):
        """
        Distillation impact.
        """
        return {
            'teacher_size': '100%',
            'student_size': '5-10%',
            'accuracy_retention': '90-95% of teacher',
            'speedup': '10-20x'
        }

class EdgeGPUInference:
    """
    Optimize for edge GPUs.
    """
    
    def tensorrt_optimization(self, model):
        """
        NVIDIA TensorRT optimization.
        """
        # Convert to TensorRT
        import tensorrt as trt
        
        logger = trt.Logger(trt.Logger.WARNING)
        builder = trt.Builder(logger)
        network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
        parser = trt.OnnxParser(network, logger)
        
        # Parse ONNX model
        with open('model.onnx', 'rb') as f:
            parser.parse(f.read())
        
        # Build engine
        config = builder.create_builder_config()
        config.max_workspace_size = 1 << 30  # 1GB
        
        engine = builder.build_serialized_network(network, config)
        
        return engine
    
    def optimizations(self):
        """
        TensorRT techniques.
        """
        return {
            'fp16': 'Half precision for 2x speed',
            'int8': 'INT8 quantization',
            'layer_fusion': 'Merge layers',
            'kernel_auto_tuning': 'Select best GPU kernels',
            'memory_optimization': 'Reuse buffers'
        }

class OnDeviceFrameworks:
    """
    Deploy ML on edge devices.
    """
    
    def tensorflow_lite(self):
        """
        TensorFlow Lite for mobile/embedded.
        """
        # Convert model
        converter = tf.lite.TFLiteConverter.from_keras_model(model)
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        converter.target_spec.supported_types = [tf.float16]
        
        tflite_model = converter.convert()
        
        # Inference
        interpreter = tf.lite.Interpreter(model_content=tflite_model)
        interpreter.allocate_tensors()
        
        input_details = interpreter.get_input_details()
        output_details = interpreter.get_output_details()
        
        interpreter.set_tensor(input_details[0]['index'], input_data)
        interpreter.invoke()
        
        output = interpreter.get_tensor(output_details[0]['index'])
        
        return output
    
    def coreml(self):
        """
        Apple's Core ML for iOS.
        """
        # Convert from PyTorch
        import coremltools as ct
        
        model = ct.convert(
            pytorch_model,
            inputs=[ct.ImageType(name="input", shape=(1, 3, 224, 224))]
        )
        
        # Optimize
        model = ct.optimize(model, ct.OptimizationMode.LOWEST_LATENCY)
        
        # Save
        model.save("model.mlmodel")
        
        return model
    
    def mnn(self):
        """
        Alibaba's MNN for cross-platform.
        """
        # High-performance edge inference
        import mnn
        
        interpreter = mnn.Interpreter("model.mnn")
        session = interpreter.create_session()
        
        input_tensor = interpreter.get_input()[0]
        input_tensor.copy_from(input_data)
        
        session.run()
        
        output_tensor = interpreter.get_output()[0]
        output = output_tensor.copy_to_numpy()
        
        return output

class EdgeComputerVision:
    """
    On-device vision applications.
    """
    
    def object_detection(self):
        """
        Real-time detection on edge.
        """
        return {
            'models': 'MobileNet-SSD, YOLOv8-nano, EfficientDet-Lite',
            'use_cases': [
                'Autonomous vehicles',
                'Retail analytics',
                'Security cameras',
                'Industrial inspection'
            ],
            'performance': '30-60 FPS on mobile'
        }
    
    def segmentation(self):
        """
        Semantic/instance segmentation.
        """
        return {
            'models': 'DeepLabV3+, MobileNetV3',
            'use_cases': [
                'AR overlay',
                'Video conferencing background',
                'Autonomous driving'
            ]
        }

class EdgeNLP:
    """
    On-device language processing.
        """
    
    def speech_recognition(self):
        """
        Offline voice transcription.
        """
        return {
            'model': 'Whisper tiny, Vosk',
            'use_cases': [
                'Voice typing',
                'Accessibility',
                'Meeting transcription'
            ],
            'offline': 'No internet required'
        }
    
    def text_processing(self):
        """
        On-device text AI.
        """
        return {
            'models': 'DistilBERT, MiniLM',
            'use_cases': [
                'Auto-complete',
                'Spelling correction',
                'Sentiment analysis'
            ],
            'size': '20-50 MB'
        }

class IoTAnalytics:
    """
    TinyML for sensor data.
    """
    
    def anomaly_detection(self):
        """
        Detect anomalies on edge.
        """
        return {
            'use_case': 'Industrial machine monitoring',
            'model': '1D CNN, LSTM',
            'data': 'Vibration, temperature, current',
            'benefits': 'Real-time alerts, no cloud'
        }
    
    def predictive_maintenance(self):
        """
        Predict failures before they happen.
        """
        return {
            'sensors': 'Accelerometer, microphone',
            'model': 'TinyML anomaly detection',
            'edge': 'Process locally, alert immediately',
            'accuracy': '90%+ with proper training'
        }

class ModelConversion:
    """
    Convert models for edge deployment.
    """
    
    def pytorch_to_tflite(self):
        """
        PyTorch → ONNX → TensorFlow → TFLite.
        """
        # PyTorch to ONNX
        torch.onnx.export(
            model,
            dummy_input,
            "model.onnx",
            input_names=['input'],
            output_names=['output']
        )
        
        # ONNX to TFLite
        converter = tf.lite.TFLiteConverter.from_onnx("model.onnx")
        tflite_model = converter.convert()
        
        return tflite_model
    
    def pytorch_to_coreml(self):
        """
        PyTorch → Core ML (iOS).
        """
        traced = torch.jit.trace(model, dummy_input)
        
        model_coreml = ct.convert(
            traced,
            inputs=[ct.ImageType(shape=(1, 3, 224, 224))]
        )
        
        return model_coreml
    
    def pytorch_to_onnx_runtime(self):
        """
        PyTorch → ONNX → ONNX Runtime (cross-platform).
        """
        import onnxruntime as ort
        
        # Save as ONNX
        torch.onnx.export(model, dummy_input, "model.onnx")
        
        # Create runtime
        sess = ort.InferenceSession("model.onnx")
        
        # Run inference
        output = sess.run(None, {"input": data})
        
        return output

class LatencyOptimization:
    """
    Reduce inference latency.
    """
    
    def batching(self):
        """
        Process multiple inputs together.
        """
        return {
            'throughput': 'Higher with batching',
            'latency': 'Individual requests wait for batch',
            'tradeoff': 'Batch size vs latency'
        }
    
    def caching(self):
        """
        Cache frequent inputs.
        """
        return {
            'technique': 'Cache inference results',
            'applicability': 'Repeated queries',
            'speedup': 'Near-instant for cache hits'
        }
    
    def pipelining(self):
        """
        Overlap preprocessing/inference/postprocessing.
        """
        return {
            'technique': 'Multi-threading, async',
            'gpu': 'Stream different stages',
            'result': 'Hide latency'
        }