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Can I read Characterizing Deep Neural Networks on Edge Computing Systems for Object Classification in 3D Point Clouds on EtoBox?
Characterizing Deep Neural Networks on Edge Computing Systems for Object Classification in 3D Point Clouds by Cristian Wisultschew; Alejandro Perez; Andres Otero; Gabriel Mujica; Jorge Portilla is a Engineering article available to read on EtoBox.
What is Characterizing Deep Neural Networks on Edge Computing Systems for Object Classification in 3D Point Clouds about?
The current trend of shifting computing from the cloud to the edge of the Internet of Things is influencing deep learning applications. Moving intelligence closer to the point of need entails advantages in terms of performance, power consumption, security, and privacy. The problem arises with data sources that generate a massive amount of information, making data processing challenging for edge devices. This is the case of point clouds generated by LIDAR sensors. Implementations at the edge become even more challenging when heavy processing algorithms such as deep neural networks are selected. However, deep neural networks are the state-of-the-art solution to carry out object classification tasks as they provide the best results in terms of accuracy when working with high data volumes. This work demonstrates that the processing of point cloud-based sensors using deep neural networks at the edge is becoming feasible with the emergence of new devices with high computing capacity combined with reduced power consumption. In this regard, a characterization of first-in-class deep learning classification algorithms working with point cloud data as inputs and running over different state-o
Who reads Characterizing Deep Neural Networks on Edge Computing Systems for Object Classification in 3D Point Clouds?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Cristian Wisultschew; Alejandro Perez; Andres Otero; Gabriel Mujica; Jorge Portilla
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Published
- 2022
- Language
- EN
- Field
- Engineering (Physical Sciences)