EdgeAI Algorithms

EdgeAI algorithms are specifically designed or optimized for deployment on resource-constrained edge devices, balancing accuracy with computational efficiency.

Efficient Neural Network Architectures

MobileNets Family

import tensorflow as tf

# MobileNetV3 implementation
def mobilenet_v3_block(x, filters, kernel_size, stride, se_ratio=0.25):
    # Depthwise separable convolution
    x = tf.keras.layers.DepthwiseConv2D(
        kernel_size, strides=stride, padding='same', use_bias=False
    )(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU6()(x)

    # Squeeze-and-Excitation
    if se_ratio:
        se = tf.keras.layers.GlobalAveragePooling2D()(x)
        se = tf.keras.layers.Dense(int(filters * se_ratio), activation='relu')(se)
        se = tf.keras.layers.Dense(filters, activation='sigmoid')(se)
        x = tf.keras.layers.Multiply()([x, se])

    # Pointwise convolution
    x = tf.keras.layers.Conv2D(filters, 1, use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)

    return x

# Performance comparison
mobilenet_variants = {
    'MobileNetV1': {'params': '4.2M', 'macs': '569M', 'top1': '70.6%'},
    'MobileNetV2': {'params': '3.4M', 'macs': '300M', 'top1': '72.0%'},
    'MobileNetV3-Small': {'params': '2.9M', 'macs': '66M', 'top1': '67.4%'},
    'MobileNetV3-Large': {'params': '5.4M', 'macs': '219M', 'top1': '75.2%'}
}

EfficientNet Architecture

Model	Parameters	FLOPs	Top-1 Accuracy	Latency (V100)
EfficientNet-B0	5.3M	0.39B	77.1%	2.9ms
EfficientNet-B1	7.8M	0.70B	79.1%	4.1ms
EfficientNet-B2	9.2M	1.0B	80.1%	4.8ms
EfficientNet-B3	12M	1.8B	81.6%	6.7ms

# EfficientNet scaling
def efficientnet_scaling(base_model, phi):
    """Scale EfficientNet based on compound scaling"""
    alpha = 1.2  # depth scaling
    beta = 1.1   # width scaling  
    gamma = 1.15 # resolution scaling

    depth_multiplier = alpha ** phi
    width_multiplier = beta ** phi
    resolution = int(224 * (gamma ** phi))

    return {
        'depth': depth_multiplier,
        'width': width_multiplier,
        'resolution': resolution
    }

Computer Vision Algorithms

Object Detection

# YOLOv5 for edge deployment
class YOLOv5Edge:
    def __init__(self, model_path, conf_threshold=0.5):
        self.model = self.load_model(model_path)
        self.conf_threshold = conf_threshold

    def detect(self, image):
        # Preprocessing
        input_tensor = self.preprocess(image)

        # Inference
        predictions = self.model(input_tensor)

        # Post-processing
        boxes, scores, classes = self.postprocess(predictions)

        return self.filter_detections(boxes, scores, classes)

    def preprocess(self, image):
        # Resize to 640x640 (YOLOv5 input size)
        resized = cv2.resize(image, (640, 640))
        normalized = resized / 255.0
        return np.expand_dims(normalized, axis=0)

# YOLO model comparison for edge
yolo_models = {
    'YOLOv5n': {'size': '1.9MB', 'mAP': '28.0%', 'fps_jetson_nano': '45'},
    'YOLOv5s': {'size': '14.1MB', 'mAP': '37.4%', 'fps_jetson_nano': '25'},
    'YOLOv5m': {'size': '42.2MB', 'mAP': '45.4%', 'fps_jetson_nano': '12'},
    'YOLOv8n': {'size': '3.2MB', 'mAP': '37.3%', 'fps_jetson_nano': '42'}
}

Image Classification

# Lightweight classification models
def create_lightweight_classifier(num_classes, input_shape=(224, 224, 3)):
    model = tf.keras.Sequential([
        # Efficient feature extraction
        tf.keras.layers.Conv2D(32, 3, strides=2, activation='relu'),
        tf.keras.layers.BatchNormalization(),

        # Depthwise separable blocks
        tf.keras.layers.SeparableConv2D(64, 3, activation='relu'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D(2),

        tf.keras.layers.SeparableConv2D(128, 3, activation='relu'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D(2),

        # Global pooling and classification
        tf.keras.layers.GlobalAveragePooling2D(),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])

    return model

# Edge-optimized architectures comparison
edge_classifiers = {
    'MobileNetV2': {'accuracy': '71.8%', 'size': '14MB', 'latency': '23ms'},
    'EfficientNet-Lite0': {'accuracy': '75.1%', 'size': '6.9MB', 'latency': '19ms'},
    'ShuffleNetV2': {'accuracy': '69.4%', 'size': '9.2MB', 'latency': '15ms'},
    'GhostNet': {'accuracy': '73.9%', 'size': '20MB', 'latency': '21ms'}
}

Natural Language Processing

Lightweight Language Models

# DistilBERT for edge NLP
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

class EdgeNLPModel:
    def __init__(self, model_name='distilbert-base-uncased'):
        self.tokenizer = DistilBertTokenizer.from_pretrained(model_name)
        self.model = DistilBertForSequenceClassification.from_pretrained(model_name)

    def predict(self, text):
        inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True)
        outputs = self.model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        return predictions.detach().numpy()

# NLP model comparison for edge
nlp_models = {
    'BERT-Base': {'params': '110M', 'size': '440MB', 'latency': '180ms'},
    'DistilBERT': {'params': '66M', 'size': '268MB', 'latency': '95ms'},
    'TinyBERT': {'params': '14.5M', 'size': '58MB', 'latency': '25ms'},
    'MobileBERT': {'params': '25.3M', 'size': '103MB', 'latency': '45ms'}
}

Text Processing Algorithms

# Efficient text classification
class LightweightTextClassifier:
    def __init__(self, vocab_size=10000, embedding_dim=128):
        self.model = tf.keras.Sequential([
            tf.keras.layers.Embedding(vocab_size, embedding_dim),
            tf.keras.layers.GlobalAveragePooling1D(),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dropout(0.5),
            tf.keras.layers.Dense(1, activation='sigmoid')
        ])

    def train(self, x_train, y_train, epochs=10):
        self.model.compile(
            optimizer='adam',
            loss='binary_crossentropy',
            metrics=['accuracy']
        )

        return self.model.fit(x_train, y_train, epochs=epochs, validation_split=0.2)

Time Series and Sensor Data

LSTM for Edge

# Lightweight LSTM for sensor data
def create_edge_lstm(sequence_length, features, units=32):
    model = tf.keras.Sequential([
        tf.keras.layers.LSTM(units, return_sequences=True, input_shape=(sequence_length, features)),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.LSTM(units//2),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(1)
    ])

    return model

# Sensor data processing example
class SensorDataProcessor:
    def __init__(self, window_size=100):
        self.window_size = window_size
        self.scaler = StandardScaler()

    def preprocess_sensor_data(self, raw_data):
        # Normalize data
        normalized = self.scaler.fit_transform(raw_data.reshape(-1, 1))

        # Create sequences
        sequences = []
        for i in range(len(normalized) - self.window_size):
            sequences.append(normalized[i:i+self.window_size])

        return np.array(sequences)

# Edge time series models performance
timeseries_models = {
    'LSTM-32': {'params': '15K', 'latency': '5ms', 'accuracy': '94.2%'},
    'GRU-24': {'params': '11K', 'latency': '3ms', 'accuracy': '93.8%'},
    'TCN-16': {'params': '8K', 'latency': '2ms', 'accuracy': '92.1%'},
    'Transformer-Mini': {'params': '45K', 'latency': '12ms', 'accuracy': '95.1%'}
}

Optimization Techniques

Quantization Algorithms

# Post-training quantization
def quantize_weights(model, quantization_bits=8):
    """Quantize model weights to reduce size"""
    quantized_weights = []

    for layer in model.layers:
        if hasattr(layer, 'get_weights') and layer.get_weights():
            weights = layer.get_weights()
            quantized_layer_weights = []

            for weight in weights:
                # Calculate scale and zero point
                min_val = np.min(weight)
                max_val = np.max(weight)
                scale = (max_val - min_val) / (2**quantization_bits - 1)
                zero_point = -min_val / scale

                # Quantize
                quantized = np.round(weight / scale + zero_point)
                quantized = np.clip(quantized, 0, 2**quantization_bits - 1)

                quantized_layer_weights.append(quantized.astype(np.uint8))

            quantized_weights.append(quantized_layer_weights)

    return quantized_weights

# Quantization impact analysis
quantization_analysis = {
    'fp32_original': {'size': '25.2MB', 'accuracy': '76.1%', 'latency': '45ms'},
    'int8_quantized': {'size': '6.4MB', 'accuracy': '75.3%', 'latency': '18ms'},
    'int4_quantized': {'size': '3.2MB', 'accuracy': '73.8%', 'latency': '12ms'},
    'binary_quantized': {'size': '0.8MB', 'accuracy': '68.2%', 'latency': '8ms'}
}

Pruning Algorithms

import tensorflow_model_optimization as tfmot

# Structured pruning
def apply_structured_pruning(model, sparsity=0.5):
    """Apply structured pruning to reduce model complexity"""

    pruning_params = {
        'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(
            initial_sparsity=0.0,
            final_sparsity=sparsity,
            begin_step=1000,
            end_step=5000
        )
    }

    pruned_model = tfmot.sparsity.keras.prune_low_magnitude(
        model, **pruning_params
    )

    return pruned_model

# Pruning effectiveness
pruning_results = {
    'unpruned': {'params': '25.6M', 'accuracy': '76.0%', 'flops': '4.1B'},
    '50%_pruned': {'params': '12.8M', 'accuracy': '75.2%', 'flops': '2.1B'},
    '75%_pruned': {'params': '6.4M', 'accuracy': '73.8%', 'flops': '1.0B'},
    '90%_pruned': {'params': '2.6M', 'accuracy': '70.1%', 'flops': '0.4B'}
}

Federated Learning Algorithms

# Federated averaging algorithm
class FederatedAveraging:
    def __init__(self, global_model):
        self.global_model = global_model
        self.client_models = []

    def aggregate_weights(self, client_weights, client_sizes):
        """Aggregate client model weights using FedAvg"""
        total_size = sum(client_sizes)

        # Initialize aggregated weights
        aggregated_weights = [np.zeros_like(w) for w in client_weights[0]]

        # Weighted average
        for client_w, size in zip(client_weights, client_sizes):
            weight = size / total_size
            for i, layer_weights in enumerate(client_w):
                aggregated_weights[i] += weight * layer_weights

        return aggregated_weights

    def federated_round(self, selected_clients):
        """Execute one round of federated learning"""
        client_weights = []
        client_sizes = []

        for client in selected_clients:
            # Local training
            local_weights = client.local_training()
            client_weights.append(local_weights)
            client_sizes.append(len(client.local_data))

        # Aggregate and update global model
        global_weights = self.aggregate_weights(client_weights, client_sizes)
        self.global_model.set_weights(global_weights)

        return global_weights

# Federated learning performance
federated_metrics = {
    'communication_rounds': 100,
    'participating_clients': 50,
    'local_epochs': 5,
    'final_accuracy': '94.2%',
    'communication_cost': '2.3MB per round',
    'privacy_preserved': True
}

Performance Benchmarks

Algorithm Efficiency Comparison

Algorithm Type	Model	Size	Latency	Accuracy	Power
Classification	MobileNetV3	5.4MB	15ms	75.2%	2.1W
Detection	YOLOv5n	1.9MB	22ms	28.0% mAP	3.2W
NLP	DistilBERT	268MB	95ms	92.8% F1	4.5W
Time Series	LSTM-32	60KB	5ms	94.2%	0.8W

Edge-Specific Optimizations

# Algorithm selection based on constraints
def select_algorithm(constraints):
    """Select optimal algorithm based on edge constraints"""

    algorithms = {
        'ultra_low_power': {
            'classification': 'MobileNetV3-Small',
            'detection': 'YOLOv5n',
            'nlp': 'TinyBERT',
            'timeseries': 'GRU-16'
        },
        'balanced': {
            'classification': 'EfficientNet-B0',
            'detection': 'YOLOv5s',
            'nlp': 'DistilBERT',
            'timeseries': 'LSTM-32'
        },
        'high_accuracy': {
            'classification': 'EfficientNet-B3',
            'detection': 'YOLOv5m',
            'nlp': 'MobileBERT',
            'timeseries': 'Transformer-Mini'
        }
    }

    if constraints['power'] < 3 and constraints['latency'] < 20:
        return algorithms['ultra_low_power']
    elif constraints['accuracy'] > 90:
        return algorithms['high_accuracy']
    else:
        return algorithms['balanced']

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Next: Applications - Real-world EdgeAI use cases and implementations.