A comprehensive survey on model compression and acceleration

A comprehensive survey on model compression and acceleration-i


In recent years, machine learning (ML) and deep learning (DL) have shown remarkable improvement in computer vision, natural language processing, stock prediction, forecasting, and audio processing to name a few. The size of the trained DL model is large for these complex tasks, which makes it difficult to deploy on resource-constrained devices. For instance, size of the pre-trained VGG16 model trained on the ImageNet dataset is more than 500 MB. Resource-constrained devices such as mobile phones and internet of things devices have limited memory and less computation power. For real-time applications, the trained models should be deployed on resource-constrained devices. Popular convolutional neural network models have millions of parameters that leads to increase in the size of the trained model. Hence, it becomes essential to compress and accelerate these models before deploying on resource-constrained devices while making the least compromise with the model accuracy. It is a challenging task to retain the same accuracy after compressing the model. To address this challenge, in the last couple of years many researchers have suggested different techniques for model compression and acceleration. In this paper, we have presented a survey of various techniques suggested for compressing and accelerating the ML and DL models. We have also discussed the challenges of the existing techniques and have provided future research directions in the field. Read More

Publication: Artificial Intelligence Review

Publisher: Springer Nature

Authors: Tejalal Choudhary, Vipul Mishra, Anurag Goswami & Jagannathan Sarangapani

Keywords: Model Compression and Acceleration, Machine Learning, Deep Learning, CNN, RNN, Resource-Constrained Devices, Efficient Neural Networks

Meet one of the Author:

Tejalal Choudhary

Dr Tejalal Choudhary received his Ph.D. in Computer Science Engineering from Bennett University in Greater Noida, India. He has expertise in computer vision, machine learning, deep learning, and model compression. His exceptional ability to think creatively, tackle problems from multiple angles, and generate innovative solutions sets him apart from others.

Dr. Tejalal Choudhary

Ph.D. computer science


Bennett University, Greater Noida, India – Bennett University was established in the year 2016 by the Times Group which is India’s largest media conglomerate, to provide Ivy League quality education to undergraduate and postgraduate students. The six schools with 30+ programs and 70+ leading specialisations in Engineering, Management, Media, Law and Liberal Arts have positioned it as one of the top universities in India.

Missouri University of Science and Technology – Missouri University of Science and Technology (Missouri S&T) is a world-class technological research university. Founded in 1870 as one of the first technological schools west of the Mississippi, Missouri S&T offers a broad array of degrees in engineering, the sciences, business, information technology, the humanities and liberal arts, and education — all in an environment that emphasizes technological literacy.

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