Abstract

Deep neural networks (DNN) achieved significant breakthrough in vision recognition in 2012 and quickly became the leading machine learning algorithm in Big Data based large scale object recognition applications. The successful deployment of DNN based applications pose challenges for a cross platform software framework that enable multiple user scenarios, including offline model training on HPC clusters and online recognition in embedded environments. Existing DNN frameworks are mostly focused on a closed format CUDA implementations, which is limiting of deploy breadth of DNN hardware systems.

Keywords:
Computer science Software deployment CUDA Deep learning Limiting Implementation Artificial intelligence Computer architecture Software Artificial neural network Deep neural networks Big data Cognitive neuroscience of visual object recognition Scheme (mathematics) Machine learning Distributed computing Parallel computing Feature extraction Operating system Software engineering

Metrics

30
Cited By
2.34
FWCI (Field Weighted Citation Impact)
3
Refs
0.93
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Neural Network Applications
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Brain Tumor Detection and Classification
Life Sciences →  Neuroscience →  Neurology
© 2026 ScienceGate Book Chapters — All rights reserved.