Efficient acceleration of deep learning inference on resource-constrained edge devices: A review

MMH Shuvo, SK Islam, J Cheng… - Proceedings of the …, 2022 - ieeexplore.ieee.org
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

[HTML][HTML] Review of image classification algorithms based on convolutional neural networks

L Chen, S Li, Q Bai, J Yang, S Jiang, Y Miao - Remote Sensing, 2021 - mdpi.com
Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Run, don't walk: chasing higher FLOPS for faster neural networks

J Chen, S Kao, H He, W Zhuo, S Wen… - Proceedings of the …, 2023 - openaccess.thecvf.com
To design fast neural networks, many works have been focusing on reducing the number of
floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does …

A review of convolutional neural network architectures and their optimizations

S Cong, Y Zhou - Artificial Intelligence Review, 2023 - Springer
The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Model compression for deep neural networks: A survey

Z Li, H Li, L Meng - Computers, 2023 - mdpi.com
Currently, with the rapid development of deep learning, deep neural networks (DNNs) have
been widely applied in various computer vision tasks. However, in the pursuit of …

A survey of convolutional neural networks: analysis, applications, and prospects

Z Li, F Liu, W Yang, S Peng… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …