Lightweight Deep Learning: Introduction, Advancements, and Applications

Lightweight Deep Learning: Introduction, Advancements, and Applications

Hari Kishan Kondaveeti, Valli Kumari Vatsavayi, Srileakhana Mangapathi, Reddy M. Yasaswini
Copyright: © 2023 |Pages: 18
DOI: 10.4018/978-1-6684-8386-2.ch012
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Abstract

Lightweight deep learning is a subfield of artificial intelligence and machine learning that prioritises efficiency and compactness while developing deep learning models. It is ideal for low-powered mobile phones, embedded systems, and internet-of-things devices due to their speed and low latency. To make lightweight deep learning models, pruning and quantization are used to remove unnecessary parameters and reduce model weight accuracy. Transfer learning is used to fine-tune a pre-trained deep learning model on a smaller dataset. This chapter introduces the fundamentals of lightweight deep learning, including various lightweight models and their applications across different industries.
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Advantages Of Lightweight Deep Learning

Lightweight deep learning has several advantages over traditional deep learning techniques. In this section, we will discuss some of the significant benefits of lightweight deep learning.

Faster Inference Times

Lightweight deep learning models are significantly smaller than their full-scale counterparts, resulting in faster inference times. This makes them ideal for use cases where real-time processing is critical, such as image or video analysis, natural language processing, and other applications that require quick decision-making.

Lower Memory Usage

Smaller models also require less memory to operate, making them more efficient in resource-constrained environments. This is particularly beneficial for mobile and embedded devices, which have limited resources.

Better Power Efficiency

With lower memory usage and faster inference times, lightweight deep learning models are more power-efficient than traditional deep learning models. This is important for battery-powered devices, such as smartphones, wearables, and IoT devices.

Reduced Hardware Requirements

By creating smaller models, lightweight deep learning reduces hardware requirements for inference. This results in lower hardware costs, making it possible to deploy deep learning models in resource-constrained environments or in applications where cost is a significant factor.

Improved Privacy and Security

Lightweight deep learning reduces the amount of data transferred between devices and servers, protecting user privacy and reducing the risk of sensitive data being exposed. This is particularly important for applications that deal with personal or confidential data.

Flexibility and Adaptability

Lightweight deep learning models are highly adaptable and can be tailored to specific use cases, making them more flexible than traditional deep learning models. This means that they can be optimized for specific hardware or software platforms, making them easier to deploy and maintain.

Broad Range of Applications

lightweight deep learning makes it possible to deploy models in a broader range of applications, including those in developing countries or remote areas with limited access to high-end hardware. This can have a significant impact on industries such as healthcare, agriculture, and education, where access to technology is limited.

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