Sequence Models
What is Sequence Models?
Sequence models, also called sequential neural networks, are AI algorithms designed to analyze and predict data that unfolds over time. In Edge AI, they process real-time sensor, IoT, or device-generated data locally, enabling fast and intelligent decision-making at the network edge.
Why Is It Used?
They are used to identify patterns, trends, and dependencies in time-series or sequential data, allowing predictive analytics, anomaly detection, and automation directly on edge devices.
How Is It Used?
Monitoring industrial equipment for early fault detection
Predicting traffic or user behavior in real-time
Enhancing smart devices with adaptive responses
Local processing of IoT sensor streams to reduce latency
Types of Sequence Models
Recurrent Neural Networks (RNNs): Handle temporal dependencies.
Long Short-Term Memory (LSTM) Networks: Capture long-term patterns.
Gated Recurrent Units (GRUs): Lightweight alternatives for edge deployment.
Transformer Models: Efficiently model complex sequences with attention mechanisms.
Benefits of Sequence Models
Low-latency predictions at the edge
Reduced bandwidth and cloud dependency
Real-time automation and anomaly detection
Optimized energy and computational efficiency for IoT devices