18119488. MULTI-PLATFORM NEURAL NETWORK DEPLOYMENT simplified abstract (Adobe Inc.)
Contents
MULTI-PLATFORM NEURAL NETWORK DEPLOYMENT
Organization Name
Inventor(s)
Zichuan Liu of San Jose CA (US)
Wentian Zhao of San Jose CA (US)
MULTI-PLATFORM NEURAL NETWORK DEPLOYMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18119488 titled 'MULTI-PLATFORM NEURAL NETWORK DEPLOYMENT
Simplified Explanation:
The patent application describes a method for converting a machine learning model for execution by a computing device. This involves generating a computing graph based on the model, detecting sub-structures within the graph, and optimizing the graph for efficient execution.
Key Features and Innovation:
- Generation of a computing graph based on a machine learning model.
- Detection and combination of sub-graphs within the computing graph.
- Optimization of the computing graph for improved performance.
- Creation of net-list and weight objects for inferencing operations.
Potential Applications: The technology can be applied in various fields such as image recognition, natural language processing, and autonomous driving systems.
Problems Solved: The technology addresses the need for efficient execution of machine learning models on computing devices.
Benefits:
- Improved performance and efficiency of machine learning models.
- Enhanced inferencing operations on computing devices.
Commercial Applications: Potential commercial applications include AI-powered software, autonomous systems, and data analytics platforms.
Questions about the Technology: 1. How does the technology optimize the computing graph for efficient execution? 2. What are the potential limitations of converting machine learning models for execution on computing devices?
Frequently Updated Research: Stay updated on advancements in machine learning model optimization and inferencing operations for computing devices.
Original Abstract Submitted
In various examples, a machine learning model is converted for execution by a computing device. For example, a computing graph is generated based on the machine learning model and sub-graphs within the computing graph that match sub-structures that are detected and combined into a vertex to generate an optimized computing graph. A net-list object and weight object are then generated based on the optimized computing graph and provided to the computing device to enable inferencing operations.
