Model pruning is a technique used to reduce the size of a neural network by eliminating redundant or less important neurons and weights, improving computational efficiency without significantly affecting performance. This process allows for faster inference times and reduced resource consumption, making it particularly valuable in deploying models on edge devices or in resource-constrained environments.

Model Pruning

Model pruning

A neural network is a computational model inspired by the human brain's network of neurons, designed to recognize patterns and solve complex problems through learning from data. It consists of layers of interconnected nodes that adjust their weights based on input data to improve accuracy over time.

neural network

Reducing the size

Redundant neurons

less important neurons

Computational efficiency refers to the effectiveness of an algorithm in terms of both time and space resources used to solve a problem. It is crucial in optimizing performance, especially in large-scale computations and real-time processing, where resource constraints are significant.

computational efficiency

inference times

Resource consumption refers to the use of natural, human, and economic resources to produce goods and services, impacting both the environment and economic systems. It is crucial to balance consumption with sustainable practices to ensure resources are available for future generations while maintaining economic growth.

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