Pruning
What is Pruning?
Pruning is a technique in Edge AI that removes unnecessary connections or parameters from machine learning models to improve efficiency without sacrificing accuracy. Also known as model slimming, it reduces computational load, enabling AI models to run faster on edge devices with limited resources.
Pruning is the process of eliminating redundant or non-critical neurons, weights, or connections in neural networks to make AI models lighter and faster. This model optimization is crucial for deploying intelligent systems directly on edge devices like IoT sensors or mobile hardware.
Why Is It Used?
Edge devices have limited processing power and memory. Pruning is used to reduce model size and complexity, enabling real-time decision-making, lower latency, and efficient energy usage without compromising predictive performance.
How Is It Used?
Identify low-importance weights or neurons in a trained model.
Remove or zero out these parameters.
Retrain the model to maintain accuracy.
Deploy the optimized, pruned model to edge devices for faster inference.
Types of Pruning
Structured Pruning – Removes entire neurons, channels, or layers for hardware-friendly optimization.
Unstructured Pruning – Removes individual weights or connections, resulting in sparse models.
Dynamic Pruning – Adjusts pruning on-the-fly during runtime based on usage or data patterns.
Benefits of Pruning
Reduced Model Size: Saves storage space on edge devices.
Faster Inference: Enables real-time AI decisions.
Lower Energy Consumption: Critical for battery-operated IoT devices.
Cost-Efficiency: Minimizes cloud dependency by processing AI at the edge.