Micron technology, inc. (20240202521). ARTIFICIAL NEURAL NETWORK TRAINING USING EDGE DEVICES simplified abstract
Contents
ARTIFICIAL NEURAL NETWORK TRAINING USING EDGE DEVICES
Organization Name
Inventor(s)
Pavana Prakash of Houston TX (US)
Shashank Bangalore Lakshman of Folsom CA (US)
Saideep Tiku of Fort Collins CO (US)
ARTIFICIAL NEURAL NETWORK TRAINING USING EDGE DEVICES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202521 titled 'ARTIFICIAL NEURAL NETWORK TRAINING USING EDGE DEVICES
Simplified Explanation: The patent application describes a method for training an Artificial Neural Network (ANN) by providing an initial ANN model to multiple groups of edge devices, receiving input from one group, and using activation signals from other groups to refine the model.
- Initial ANN model provided to multiple groups of edge devices
- Input received from one group of edge devices
- Activation signals received from other groups of edge devices
- Refining the model based on training feedback from different activation signals
- Trained ANN model generated from the plurality of groups of edge devices
Key Features and Innovation: - Training an ANN model using multiple groups of edge devices - Utilizing activation signals from different groups for training feedback - Generating a trained ANN model based on the received feedback
Potential Applications: - Edge computing - Machine learning - Internet of Things (IoT) devices
Problems Solved: - Efficient training of ANN models using distributed edge devices - Enhancing the accuracy and performance of ANN models through collaborative training
Benefits: - Improved scalability and speed of training ANN models - Enhanced accuracy and reliability of trained models - Utilization of edge devices for distributed computing tasks
Commercial Applications: Title: Distributed Edge Device Training for Artificial Neural Networks This technology can be applied in industries such as: - Healthcare for medical image analysis - Manufacturing for predictive maintenance - Finance for fraud detection systems
Prior Art: Prior research in distributed machine learning and edge computing technologies can provide insights into similar approaches to training ANN models using multiple devices.
Frequently Updated Research: Stay updated on advancements in distributed computing, edge AI, and machine learning techniques for training neural networks.
Questions about Artificial Neural Network Training: 1. How does training an ANN model using multiple groups of edge devices improve performance compared to traditional methods? 2. What are the potential challenges in implementing a distributed training approach for ANN models?
Original Abstract Submitted
training the ann can include providing an initial ann model to a plurality of groups of edge devices and providing an input to a group of edge devices from the plurality of groups of edge devices. training the ann can also include, responsive to providing the input, receiving activation signals from a first portion of the plurality of groups. training the ann can include providing the activation signals to a second portion of the plurality of groups and provide commands to the plurality of groups of edge devices to train the initial ann model to generate a trained ann model based on training feedback generated using different activation signals received from the second portion of the plurality of groups. training the ann can also include receiving the trained ann model from the plurality of groups of edge devices.
