Neural Network
What is Neural Network?
A neural network is a computational model inspired by the human brain that processes information through interconnected nodes, or “neurons.” In Edge AI, neural networks enable devices to learn, reason, and make intelligent decisions locally—reducing reliance on cloud computation and latency. It’s the foundation of real-time, on-device intelligence.
Why Is It Used?
Neural networks are used in Edge AI to process data closer to where it’s generated—on IoT devices, sensors, and gateways. This reduces bandwidth usage, enhances privacy, and delivers instant analytics without needing continuous cloud connectivity.
How Is It Used?
At the edge, neural networks are deployed in optimized or compressed formats (e.g., quantized models) to run efficiently on constrained hardware.
Use cases include:
Smart cameras detecting anomalies in real time
Predictive maintenance in industrial IoT systems
Autonomous vehicle perception and navigation
Energy optimization in connected infrastructure
Types of Neural Network
Convolutional Neural Networks (CNNs): For image and video recognition on edge devices
Recurrent Neural Networks (RNNs): For sequence or time-series data processing
Spiking Neural Networks (SNNs): Neuromorphic models mimicking brain activity for ultra-efficient inference
Transformer Models: Lightweight versions powering real-time natural language tasks on edge endpoints
Benefits of Neural Network
Low Latency: Instant decision-making at the device level
Data Privacy: Sensitive data stays on-device
Reduced Cloud Costs: Lower data transfer and processing expenses
Scalability: Enables millions of intelligent endpoints
Resilience: Functions even in low or no-connectivity environments