17942039. HIERARCHICAL PROGRAMMING MODEL FOR ARTIFICIAL INTELLIGENCE HARDWARE simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)
Contents
- 1 HIERARCHICAL PROGRAMMING MODEL FOR ARTIFICIAL INTELLIGENCE HARDWARE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 HIERARCHICAL PROGRAMMING MODEL FOR ARTIFICIAL INTELLIGENCE HARDWARE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.9.1 Unanswered Questions
- 1.9.2 How does this hierarchical programming model compare to existing AI hardware architectures in terms of performance and scalability?
- 1.9.3 What are the potential limitations or challenges in implementing this hierarchical programming model in real-world AI hardware systems?
- 1.10 Original Abstract Submitted
HIERARCHICAL PROGRAMMING MODEL FOR ARTIFICIAL INTELLIGENCE HARDWARE
Organization Name
MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor(s)
Haishan Zhu of Bellevue WA (US)
Eric S. Chung of Woodinville WA (US)
HIERARCHICAL PROGRAMMING MODEL FOR ARTIFICIAL INTELLIGENCE HARDWARE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17942039 titled 'HIERARCHICAL PROGRAMMING MODEL FOR ARTIFICIAL INTELLIGENCE HARDWARE
Simplified Explanation
The present disclosure describes a hierarchical programming model for AI hardware, including lower-level control threads and a higher-level control thread that generates commands for the lower-level threads to execute.
- The system includes a set of lower-level control threads responsible for executing commands.
- A higher-level control thread receives commands from a device, generates a set of commands, and distributes them to a subset of lower-level control threads.
- Each lower-level control thread instructs a subset of processing threads to perform a set of operations based on the commands received.
Potential Applications
This technology could be applied in various industries such as robotics, autonomous vehicles, and industrial automation where complex tasks need to be executed efficiently by AI hardware.
Problems Solved
This hierarchical programming model helps in organizing and optimizing the execution of commands by AI hardware, leading to improved performance and efficiency in processing tasks.
Benefits
The system allows for better coordination and distribution of tasks among processing threads, leading to faster and more accurate execution of commands. It also simplifies the programming process for developers working with AI hardware.
Potential Commercial Applications
Potential commercial applications of this technology include AI-powered systems in manufacturing, healthcare, and smart infrastructure where efficient task execution is crucial for optimal performance.
Possible Prior Art
One possible prior art could be the use of multi-threading techniques in computer systems to improve task execution and performance. However, the specific hierarchical programming model described in this disclosure may offer unique advantages in the context of AI hardware.
Unanswered Questions
How does this hierarchical programming model compare to existing AI hardware architectures in terms of performance and scalability?
The article does not provide a direct comparison with existing AI hardware architectures, so it is unclear how this hierarchical programming model stacks up in terms of performance and scalability.
What are the potential limitations or challenges in implementing this hierarchical programming model in real-world AI hardware systems?
The article does not address any potential limitations or challenges that may arise in implementing this hierarchical programming model in practical AI hardware systems.
Original Abstract Submitted
Embodiments of the present disclosure include systems and methods for providing a hierarchical programming model for AI hardware. A system includes a set of lower-level control threads. The system also includes a higher-level control thread configured to receive a command from a device, generate a set of commands based on the command, and provide the set of commands to a subset of the set of lower-level control threads. A lower-level control thread in the subset of the set of lower-level control threads is configured to instruct, based on a particular command in the set of commands, a subset of a plurality of processing threads to perform a set of operations.
