18179629. NEURAL NETWORK MODEL PARTITIONING IN A WIRELESS COMMUNICATION SYSTEM simplified abstract (QUALCOMM Incorporated)
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 18179629 titled 'NEURAL NETWORK MODEL PARTITIONING IN A WIRELESS COMMUNICATION SYSTEM
The abstract describes methods, systems, and devices for wireless communication, where a neural network model is partitioned between two devices.
- A first device selects a partition layer for partitioning the neural network model.
- The first device implements a first sub-neural network model with the partition layer.
- A second device implements a second sub-neural network model with a layer adjacent to the partition layer.
Potential Applications: - This technology can be applied in wireless communication systems to optimize neural network processing between devices. - It can be used in IoT devices for efficient data processing and communication. - Autonomous vehicles can benefit from this technology for real-time decision-making processes.
Problems Solved: - Enables efficient partitioning of neural network models between devices. - Improves communication and processing speed in wireless systems. - Enhances the scalability and performance of neural network applications.
Benefits: - Faster and more efficient wireless communication. - Improved processing capabilities for IoT devices. - Enhanced performance and decision-making in autonomous systems.
Commercial Applications: Title: Wireless Communication Optimization Technology for IoT and Autonomous Systems This technology can be commercialized in industries such as telecommunications, IoT device manufacturing, and autonomous vehicle development. It can improve data processing speed, communication efficiency, and overall system performance.
Questions about Wireless Communication Optimization Technology: 1. How does partitioning a neural network model between devices improve wireless communication efficiency? 2. What are the potential challenges in implementing this technology in real-world applications?
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.
