Qualcomm incorporated (20240303463). NEURAL NETWORK MODEL PARTITIONING IN A WIRELESS COMMUNICATION SYSTEM simplified abstract
Contents
NEURAL NETWORK MODEL PARTITIONING IN A WIRELESS COMMUNICATION SYSTEM
Organization Name
Inventor(s)
Kyle Chi Guan of New York NY (US)
Hong Cheng of Basking Ridge NJ (US)
Qing Li of PRINCETON JUNCTION NJ (US)
Kapil Gulati of Belle Mead NJ (US)
Himaja Kesavareddigari of Bridgewater NJ (US)
Mahmoud Ashour of San Diego CA (US)
NEURAL NETWORK MODEL PARTITIONING IN A WIRELESS COMMUNICATION SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303463 titled 'NEURAL NETWORK MODEL PARTITIONING IN A WIRELESS COMMUNICATION SYSTEM
Simplified Explanation: The patent application describes methods, systems, and devices for wireless communication, where a neural network model is partitioned between two devices to improve efficiency.
- The first device selects a partition layer for dividing the neural network model.
- The first device implements a sub-neural network model with the partition layer.
- The second device implements a sub-neural network model with a layer adjacent to the partition layer.
Key Features and Innovation:
- Partitioning a neural network model between devices for wireless communication.
- Implementing sub-neural network models on different devices for efficient processing.
Potential Applications: This technology can be applied in various wireless communication systems, such as IoT devices, autonomous vehicles, and smart home devices.
Problems Solved:
- Enhances efficiency in wireless communication systems.
- Optimizes processing of neural network models across multiple devices.
Benefits:
- Improved performance in wireless communication.
- Enhanced scalability and flexibility in neural network processing.
Commercial Applications: Potential commercial applications include telecommunications, smart devices, and industrial automation systems. This technology can lead to more efficient and reliable wireless communication solutions.
Questions about Wireless Communication: 1. How does partitioning a neural network model between devices improve efficiency in wireless communication? 2. What are the key benefits of implementing sub-neural network models on different devices for wireless communication systems?
Original Abstract Submitted
methods, systems, and devices for wireless communication are described. a first device may select a partition layer for partitioning a neural network model between the first device and a second device. the first device may implement a first sub-neural network model that includes the partition layer and the second device may implement a second sub-neural network model that includes a layer adjacent to the partition layer.
