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Improving Neural Network Efficiency Through Advanced Pruning Technique

Leo Migdal
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improving neural network efficiency through advanced pruning technique

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Use of this web site signifies your agreement to the terms and conditions. Deep neural networks (DNNs) have become popular for their ability to perform complex tasks, sometimes even better than humans. They are widely used in areas like computer vision, natural language processing, and many other fields. However, despite their impressive performance, these models require a lot of computational power and memory. This can lead to slow processing times, especially during inference, which is the phase where the model makes predictions based on new data. One effective way of addressing these issues is through a process called Pruning.

Pruning involves removing parts of the model that are not very important. This helps to reduce the size of the model and makes it faster without losing much accuracy. However, there are challenges in how to effectively prune a model while still keeping it accurate. Pruning is essential for making DNNs suitable for real-world applications. When a model is pruned, it can run faster and use less memory. This is particularly important for devices with limited resources, like smartphones or embedded systems.

Traditional dense models, which contain many parameters, can be slow and inefficient. The aim of pruning is to transform these dense models into sparse models, which contain fewer parameters. However, the main challenge of network pruning is to strike a balance between maintaining the model's accuracy and improving its efficiency. The way that weights are removed during pruning has a significant impact on this balance. There are various ways to implement pruning. The most straightforward method is called element-wise (EW) pruning.

In this approach, individual weights are removed based on their importance. The weights with the least importance are pruned first. While this may seem efficient, it often leads to a disorganized pattern of remaining weights, resulting in unstructured memory access. This can slow down processing because the hardware is not optimized for such irregularities. In recent years, large neural networks have gained popularity because they tend to perform better than smaller ones in various tasks like image recognition and language processing. However, these large models often require a lot of resources, which can be a problem, especially for devices with limited power, such as mobile phones or embedded systems.

To tackle this issue, researchers have been working on a method known as "Network Pruning," which helps to reduce the size of these networks while keeping their performance intact. Network pruning involves removing parts of a neural network that may not contribute significantly to its performance. By cutting away unnecessary weights or filters, we can make the model smaller and faster. This process can help reduce storage needs, energy consumption, and the time it takes for the model to make predictions. There are two main types of pruning: weight pruning, which focuses on individual weights, and Filter Pruning, which targets entire filters. This discussion centers on filter pruning, which removes entire filters from the network.

Filter pruning is more straightforward and can be used with many existing deep learning libraries and hardware setups. The goal here is to refine the original network into a smaller version that still performs well. Previous methods for pruning often used a "train-prune-retrain" approach, which could be slow and inefficient. One significant idea introduced is the concept of "network pruning spaces." This refers to a set of different sub-networks that can be created by applying various pruning methods. Instead of just finding one best way to prune the network, exploring this range of potential sub-networks can offer valuable insights into which configurations work best under different situations. Empirical studies have revealed a few essential insights:

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

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