EdgeAI Benchmarks

Performance benchmarks and comparison studies for EdgeAI models across different hardware platforms and optimization techniques.

MLPerf Mobile Benchmarks

Image Classification Results

Device	Model	Latency	Throughput	Accuracy	Power
Pixel 6	MobileNetV2	12ms	83 FPS	71.8%	2.1W
iPhone 13	MobileNetV2	8ms	125 FPS	71.8%	1.8W
Jetson Nano	MobileNetV2	23ms	43 FPS	71.8%	5.2W
Coral TPU	MobileNetV2 (INT8)	2.5ms	400 FPS	70.9%	2.0W

Object Detection Performance

# Benchmark results for COCO dataset
detection_benchmarks = {
    'yolov5n': {
        'jetson_nano': {'fps': 45, 'mAP': 28.0, 'power': 6.8},
        'jetson_xavier_nx': {'fps': 178, 'mAP': 28.0, 'power': 15.2},
        'coral_tpu': {'fps': 120, 'mAP': 26.8, 'power': 2.3}
    },
    'ssd_mobilenet': {
        'jetson_nano': {'fps': 25, 'mAP': 23.2, 'power': 5.9},
        'raspberry_pi4': {'fps': 8, 'mAP': 23.2, 'power': 4.1},
        'intel_nuc': {'fps': 67, 'mAP': 23.2, 'power': 12.5}
    }
}

Hardware Comparison

Edge Computing Platforms

Platform	TOPS	Memory	Power	Price	Efficiency (TOPS/W)
Jetson Orin	275	32GB	60W	$1999	4.58
Jetson Xavier NX	21	8GB	25W	$399	0.84
Jetson Nano	0.5	4GB	10W	$99	0.05
Coral TPU	4	1GB	2W	$149	2.0
Intel NUC	2.9	32GB	28W	$799	0.10

Model Optimization Impact

Quantization Results

# Quantization benchmark results
quantization_results = {
    'mobilenet_v2': {
        'fp32': {'size': '14MB', 'latency': '45ms', 'accuracy': '71.8%'},
        'fp16': {'size': '7MB', 'latency': '38ms', 'accuracy': '71.6%'},
        'int8': {'size': '3.5MB', 'latency': '18ms', 'accuracy': '70.9%'}
    },
    'resnet50': {
        'fp32': {'size': '98MB', 'latency': '89ms', 'accuracy': '76.0%'},
        'fp16': {'size': '49MB', 'latency': '67ms', 'accuracy': '75.8%'},
        'int8': {'size': '25MB', 'latency': '34ms', 'accuracy': '75.1%'}
    }
}

Pruning Effectiveness

Model	Original Size	50% Pruned	75% Pruned	90% Pruned
ResNet50	98MB / 76.0%	49MB / 75.2%	25MB / 73.8%	10MB / 70.1%
MobileNetV2	14MB / 71.8%	7MB / 71.1%	3.5MB / 69.8%	1.4MB / 66.2%

Real-World Performance

Automotive ADAS Benchmarks

# ADAS system performance requirements
adas_benchmarks = {
    'object_detection': {
        'requirement': '<10ms latency',
        'jetson_orin': '3.2ms',
        'xavier_nx': '8.7ms',
        'status': 'PASS'
    },
    'lane_detection': {
        'requirement': '<5ms latency',
        'jetson_orin': '1.8ms',
        'xavier_nx': '4.2ms',
        'status': 'PASS'
    },
    'collision_prediction': {
        'requirement': '<2ms latency',
        'jetson_orin': '0.9ms',
        'xavier_nx': '2.1ms',
        'status': 'MARGINAL'
    }
}

Healthcare Edge AI

Application	Accuracy	Latency	Hardware	Clinical Validation
ECG Analysis	98.7%	<1s	Raspberry Pi 4	FDA cleared
Chest X-ray	94.1%	1.2s	Jetson Nano	CE marked
Skin Cancer	91.2%	0.5s	Mobile phone	Research only

Energy Efficiency Analysis

Power Consumption Comparison

# Energy efficiency metrics
energy_benchmarks = {
    'inference_energy': {
        'cloud_gpu': {'energy_per_inference': '15.2J', 'cost': '$0.001'},
        'jetson_nano': {'energy_per_inference': '0.23J', 'cost': '$0.00003'},
        'coral_tpu': {'energy_per_inference': '0.005J', 'cost': '$0.000001'},
        'mobile_cpu': {'energy_per_inference': '0.18J', 'cost': '$0.000025'}
    }
}

# Calculate daily energy consumption
def calculate_daily_energy(inferences_per_day, energy_per_inference):
    return inferences_per_day * energy_per_inference / 3600  # Convert to Wh

daily_energy = {
    'smart_camera': calculate_daily_energy(86400, 0.005),  # 1.2 Wh
    'mobile_app': calculate_daily_energy(100, 0.18),       # 5.0 Wh
    'iot_sensor': calculate_daily_energy(1440, 0.001)      # 0.4 Wh
}

Framework Performance Comparison

Inference Speed Comparison

Framework	MobileNetV2	ResNet50	YOLOv5s	Memory Usage
TensorFlow Lite	23ms	89ms	156ms	45MB
ONNX Runtime	19ms	67ms	134ms	38MB
PyTorch Mobile	26ms	98ms	178ms	52MB
OpenVINO	15ms	54ms	112ms	41MB

Federated Learning Benchmarks

Communication Efficiency

# Federated learning performance metrics
federated_benchmarks = {
    'cifar10_100_clients': {
        'communication_rounds': 100,
        'bytes_per_round': '2.3MB',
        'total_communication': '230MB',
        'final_accuracy': '94.2%',
        'convergence_time': '45 minutes'
    },
    'imagenet_mobile_50_clients': {
        'communication_rounds': 200,
        'bytes_per_round': '15.7MB',
        'total_communication': '3.14GB',
        'final_accuracy': '71.8%',
        'convergence_time': '6 hours'
    }
}

Benchmark Methodology

Standard Testing Protocol

class EdgeAIBenchmark:
    def __init__(self, model_path, test_dataset):
        self.model = self.load_model(model_path)
        self.test_dataset = test_dataset

    def run_comprehensive_benchmark(self):
        """Run complete benchmark suite"""

        results = {
            'accuracy': self.measure_accuracy(),
            'latency': self.measure_latency(),
            'throughput': self.measure_throughput(),
            'memory': self.measure_memory_usage(),
            'energy': self.measure_energy_consumption(),
            'model_size': self.get_model_size()
        }

        # Calculate composite scores
        results['efficiency_score'] = self.calculate_efficiency_score(results)
        results['deployment_readiness'] = self.assess_deployment_readiness(results)

        return results

    def calculate_efficiency_score(self, metrics):
        """Calculate overall efficiency score (0-100)"""

        # Weighted scoring
        weights = {
            'accuracy': 0.3,
            'latency': 0.25,
            'energy': 0.2,
            'size': 0.15,
            'throughput': 0.1
        }

        # Normalize and score each metric
        normalized_scores = self.normalize_metrics(metrics)

        efficiency_score = sum(
            weights[metric] * normalized_scores[metric]
            for metric in weights.keys()
        ) * 100

        return efficiency_score

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