Micron technology, inc. (20240184456). MANAGEMENT OF VEHICLE SYSTEM INFORMATION USING A DEEP LEARNING DEVICE simplified abstract
Contents
- 1 MANAGEMENT OF VEHICLE SYSTEM INFORMATION USING A DEEP LEARNING DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MANAGEMENT OF VEHICLE SYSTEM INFORMATION USING A DEEP LEARNING DEVICE - 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 Vehicle System Information Management
- 1.13 Original Abstract Submitted
MANAGEMENT OF VEHICLE SYSTEM INFORMATION USING A DEEP LEARNING DEVICE
Organization Name
Inventor(s)
Saideep Tiku of Folsom CA (US)
MANAGEMENT OF VEHICLE SYSTEM INFORMATION USING A DEEP LEARNING DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240184456 titled 'MANAGEMENT OF VEHICLE SYSTEM INFORMATION USING A DEEP LEARNING DEVICE
Simplified Explanation
The patent application describes methods, systems, and devices for managing vehicle system information using a deep learning device, such as a deep learning accelerator (DLA). The DLA receives information from vehicle sensors, performs operations using machine learning models, and generates analytics related to vehicle operation.
- The deep learning device, like a DLA, manages vehicle system information.
- Information from vehicle sensors is processed using machine learning models.
- The DLA can compress information to reduce resolution or frame rate.
- Analytics are generated in real-time and post-processing operations based on the compressed information.
Key Features and Innovation
- Management of vehicle system information using a deep learning device.
- Utilization of machine learning models for processing information.
- Compression of information to optimize resolution and frame rate.
- Real-time and post-processing analytics generation for vehicle operation.
Potential Applications
The technology can be applied in various industries such as automotive, transportation, and logistics for efficient management of vehicle system information.
Problems Solved
The technology addresses the need for effective management and processing of large amounts of vehicle system information to improve operational efficiency and decision-making.
Benefits
- Enhanced management of vehicle system information.
- Improved operational efficiency and decision-making.
- Optimization of resolution and frame rate for better data processing.
Commercial Applications
- "Deep Learning Device for Vehicle System Information Management": Potential commercial uses include in-vehicle systems, fleet management, and autonomous vehicles, with implications for improved data processing and decision-making in the automotive industry.
Prior Art
Information on prior art related to this technology is not provided in the abstract.
Frequently Updated Research
There is no information on frequently updated research relevant to this technology.
Questions about Vehicle System Information Management
Question 1
How does the deep learning device optimize resolution and frame rate of vehicle system information?
The deep learning device, such as a DLA, compresses the information received from vehicle sensors, which reduces the resolution and frame rate associated with the information, leading to more efficient data processing.
Question 2
What are the potential implications of using machine learning models for managing vehicle system information?
Using machine learning models allows for more advanced processing and analysis of vehicle system information, leading to improved decision-making and operational efficiency in various industries.
Original Abstract Submitted
methods, systems, and devices for management of vehicle system information using a deep learning device are described. the deep learning device of a vehicle (such as a deep learning accelerator (dla)) may receive information associated with an environment of the vehicle from one or more sensors of the vehicle. the dla may perform one or more operations using one or more machine learning models. for example, the dla may compress the information which may reduce a resolution associated with the information, a frame rate associated with the information, or both. the dla may generate, as part of a run-time operation, a first set of analytics associated with operation of the vehicle using the compressed information. additionally, or alternatively, the dla may generate, as part of a post-processing operation, a second set of analytics using the compressed or an uncompressed version of the information.
