Sample Rate
What is Sample Rate?
Sample rate refers to the number of data samples captured per second by a sensor or device. In Edge AI, it determines how frequently signals—like audio, motion, or environmental data—are collected and analyzed. A higher sample rate improves accuracy but increases data volume and processing load.
Why Is It Used?
In Edge AI systems, the sample rate ensures models receive data at the right frequency for real-time decision-making. It balances precision and efficiency—crucial for applications like predictive maintenance, voice recognition, and computer vision, where timely insights depend on rapid, localized data capture.
How Is It Used?
At the edge, devices such as sensors and embedded processors use predefined sampling frequencies to capture signals before processing them with AI models. By optimizing the sample rate, engineers can reduce bandwidth usage, latency, and power consumption while maintaining model accuracy—vital for performance-critical Edge AI deployments.
Types of Sample Rate
Low Sample Rate – Used for slow-changing signals like temperature or pressure monitoring.
High Sample Rate – Applied in fast-response systems such as vibration analysis, audio recognition, and visual inference.
Adaptive Sample Rate – Dynamically adjusts based on context or signal variability to optimize resource efficiency.
Benefits of Sample Rate
Enables real-time AI inference at the edge
Reduces data transfer and latency
Improves accuracy of predictive models
Optimizes power and processing efficiency
Enhances system scalability across diverse IoT environments