18438368. SYSTEM AND METHOD FOR PARALLELIZING CONVOLUTIONAL NEURAL NETWORKS simplified abstract (GOOGLE LLC)
Contents
- 1 SYSTEM AND METHOD FOR PARALLELIZING CONVOLUTIONAL NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR PARALLELIZING CONVOLUTIONAL NEURAL NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Parallel Convolutional Neural Networks
- 1.13 Original Abstract Submitted
SYSTEM AND METHOD FOR PARALLELIZING CONVOLUTIONAL NEURAL NETWORKS
Organization Name
Inventor(s)
Alexander Krizhevsky of Mountain View CA (US)
Ilya Sutskever of San Francisco CA (US)
Geoffrey E. Hinton of Toronto (CA)
SYSTEM AND METHOD FOR PARALLELIZING CONVOLUTIONAL NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18438368 titled 'SYSTEM AND METHOD FOR PARALLELIZING CONVOLUTIONAL NEURAL NETWORKS
Simplified Explanation
The patent application describes a parallel convolutional neural network where multiple networks operate on separate processing nodes, with some layers interconnected between nodes to forward activations.
- Each processing node has a convolutional neural network with multiple layers.
- Some layers are interconnected between nodes to pass activations forward.
- Other layers are not interconnected between nodes.
Key Features and Innovation
- Implementation of a parallel convolutional neural network with interconnected layers across processing nodes.
- Efficient utilization of multiple processing nodes for neural network operations.
Potential Applications
The technology can be applied in various fields such as image recognition, natural language processing, and pattern recognition tasks.
Problems Solved
- Improved efficiency in processing neural networks across multiple nodes.
- Enhanced performance in tasks requiring parallel processing.
Benefits
- Faster processing of neural networks.
- Increased accuracy in complex pattern recognition tasks.
- Scalability for handling large datasets.
Commercial Applications
- Potential commercial uses include in industries such as healthcare for medical image analysis, autonomous vehicles for object recognition, and cybersecurity for anomaly detection.
Prior Art
Readers can explore prior research on parallel neural networks, distributed computing, and convolutional neural network architectures.
Frequently Updated Research
Stay updated on advancements in parallel computing, neural network optimization, and distributed systems for further insights into this technology.
Questions about Parallel Convolutional Neural Networks
How does the interconnection of layers between processing nodes improve neural network performance?
The interconnection allows for the efficient sharing of activations across nodes, reducing redundant computations and enhancing overall processing speed.
What are the potential challenges in implementing a parallel convolutional neural network across multiple processing nodes?
Challenges may include synchronization issues, communication overhead between nodes, and load balancing to ensure optimal performance.
Original Abstract Submitted
A parallel convolutional neural network is provided. The CNN is implemented by a plurality of convolutional neural networks each on a respective processing node. Each CNN has a plurality of layers. A subset of the layers are interconnected between processing nodes such that activations are fed forward across nodes. The remaining subset is not so interconnected.
