18221454. NEURAL NETWORK TRAINING IN A DISTRIBUTED SYSTEM simplified abstract (Amazon Technologies, Inc.)
NEURAL NETWORK TRAINING IN A DISTRIBUTED SYSTEM
Organization Name
Inventor(s)
Vignesh Vivekraja of Santa Clara CA (US)
Thiam Khean Hah of Milpitas CA (US)
Randy Renfu Huang of Morgan Hill CA (US)
Ron Diamant of Santa Clara CA (US)
Richard John Heaton of San Jose CA (US)
NEURAL NETWORK TRAINING IN A DISTRIBUTED SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18221454 titled 'NEURAL NETWORK TRAINING IN A DISTRIBUTED SYSTEM
The abstract describes methods and systems for training a neural network, involving backward propagation computations and weight gradient exchanges between computer systems.
- Backward propagation computations are performed for the second layer of the neural network to generate weight gradients.
- The second weight gradients are split into portions for exchange between computer systems.
- A hardware interface facilitates the exchange of weight gradients between the first and second computer systems.
- Backward propagation computations are then performed for the first layer of the neural network to generate first weight gradients.
- The hardware interface transmits the first weight gradients to the second computer system.
- The remaining portions of the second weight gradients are also transmitted to the second computer system.
Potential Applications: - Deep learning systems - Artificial intelligence research - Data analysis and pattern recognition tasks
Problems Solved: - Efficient training of neural networks - Enhanced communication between computer systems - Streamlined data processing in machine learning tasks
Benefits: - Faster training times for neural networks - Improved accuracy in model predictions - Scalability for large datasets and complex networks
Commercial Applications: Title: "Advanced Neural Network Training System" This technology can be used in industries such as: - Healthcare for medical image analysis - Finance for fraud detection - Marketing for customer behavior analysis
Questions about Neural Network Training Systems: 1. How does this method improve the efficiency of training neural networks? - The method optimizes weight gradient exchanges between computer systems, reducing training times. 2. What are the potential implications of this technology in the field of artificial intelligence? - This technology can lead to more accurate and scalable AI models for various applications.
Original Abstract Submitted
Methods and systems for performing a training operation of a neural network are provided. In one example, a method comprises: performing backward propagation computations for a second layer of a neural network to generate second weight gradients; splitting the second weight gradients into portions; causing a hardware interface to exchange a first portion of the second weight gradients with the second computer system; performing backward propagation computations for a first layer of the neural network to generate first weight gradients when the exchange of the first portion of the second weight gradients is underway, the first layer being a lower layer than the second layer in the neural network; causing the hardware interface to transmit the first weight gradients to the second computer system; and causing the hardware interface to transmit the remaining portions of the second weight gradients to the second computer system.