Loss
What is Loss?
In Edge AI, loss is a metric that measures the difference between predicted outputs and actual results in machine learning models. Also called an error function, it helps quantify how accurately an Edge AI system is performing and guides optimization for real-time decision-making on devices.
Why Is It Used?
Loss is used to evaluate model performance and identify areas where predictions diverge from reality. It ensures Edge AI systems, deployed on IoT devices or edge nodes, make reliable and precise decisions without constant cloud reliance.
How Is It Used?
Calculated during training and inference to refine model weights.
Enables automated tuning of Edge AI algorithms for accuracy and efficiency.
Serves as feedback for continuous learning in local, real-time environments.
Types of Loss
Mean Squared Error (MSE): Measures squared differences for regression tasks.
Cross-Entropy Loss: Used for classification tasks, assessing probability distributions.
Huber Loss: Combines MSE and Mean Absolute Error for robust error handling.
Benefits of Loss
Enhances real-time predictive accuracy on edge devices.
Reduces unnecessary data transfer to the cloud.
Enables lightweight, energy-efficient AI operations.